tag:blogger.com,1999:blog-73324770909538985872024-02-08T06:49:12.137+01:00IT SharedWhere Knowledge meets SharingIT Sharedhttp://www.blogger.com/profile/11763288513127086508noreply@blogger.comBlogger26125tag:blogger.com,1999:blog-7332477090953898587.post-78982905523786791582017-10-24T19:04:00.000+02:002017-10-24T19:41:47.104+02:00People Decide... and so do Countries!<p>Periodically, 193 different countries gather together to make <b>important policy decisions</b> on a wide variety of <b>global topics</b>, including humanitarian crises, peace and security. Deliberations take place in the context of the <b>United Nations General Assembly</b> (UNGA) and, after consensuses have been reached, resolutions are passed.</p>
<p>Since the first General Assembly gathered in 1946, almost <b>18,000 resolutions</b> have been drafted and passed. That represents a high volume of documents to analyse for anyone who wants to <b>understand how countries decided</b> in the past (and how they might possibly decide in the future).</p>
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<p>I have recently collaborated in a contest jointly organised by the <b>OICT Department of the United Nations</b> and the <b>U.S. State Department</b>. This initiative was conceived to <b>facilitate access</b> to this important amount of information, not only for the diplomatic community but also for <b>citizens in the general public</b>.</p>
<p>In particular, my project has been awarded as finalist with the <b>second prize of the jury</b> and I have the intention to keep on applying efforts for <b>further developing</b> this tool. I am sharing this post because I would like to consider the comments and ideas from a broader group of people when it comes to thinking about <b>new features</b>, <b>enhancements </b>and <b>corrections</b> for the application.</p>
<p>Would you like to have a look? The tool is available at <b><a href="http://www.unvis.org">www.unvis.org</a></b>.</p>
<p>Please do not forget to share your feedback!</p>
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More information on <a href="https://www.uniteideas.spigit.com/Page/PCUNGAviz">UNGAViz challenge</a>.IT Sharedhttp://www.blogger.com/profile/11763288513127086508noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-55181875291299841232016-09-27T03:44:00.000+02:002016-09-28T13:05:56.349+02:00A Beginner's Guide to Apache Flink – 12 Key Terms, Explained<br />
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">In this post, I will go through 12 core Apache Flink concepts to better understand what it does and how it works. This article could perfectly serve as a beginner's overview of Flink and Streaming engine terminology.<o:p></o:p></span></div>
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Flink?<o:p></o:p></span></b></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">At first glance, the origins of Apache Flink can be traced back to
June 2008 as a researching project of the Database Systems and Information
Management (DIMA) Group at the Technische Universität (TU) Berlin in Germany.<o:p></o:p></span></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Apache Flink is an open source platform for distributed stream and
batch data processing, initially it was designed as an alternative to MapReduce
and the Hadoop Distributed File System (HFDS) in Hadoop origins.<o:p></o:p></span></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">According to the Apache Flink project, it is<i> an open source platform for distributed stream and batch data
processing. Flink’s core is a streaming dataflow engine that provides data
distribution, communication, and fault tolerance for distributed computations
over data streams. Flink also builds batch processing on top of the streaming
engine, overlaying native iteration support, managed memory, and program
optimization.”<o:p></o:p></i></span></div>
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does Flink offer?</span></b><span style="font-family: "calibri" , sans-serif; mso-ansi-language: ES-EC; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"> <span lang="ES-EC"><o:p></o:p></span></span></div>
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Java/Scala/Python), shell console, tools and Libraries to develop new data-intensive
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What does Flink’s Deploy Layer do?<o:p></o:p></span></b></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Apache Flink can execute programs in a diversity of context, such as
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</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Core Layer (Runtime)
/ What does Flink’s Core Layer do?<o:p></o:p></span></b></div>
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<br /></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">The Distributed Streaming Dataflow layer receives a program like a
generic parallel data flow with arbitrary tasks, which inputs and outputs are
data streams. This tasks are called “JobGraph”.<o:p></o:p></span></div>
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<br /></div>
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<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">4.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Stream
Processing / What is stream processing?<o:p></o:p></span></b></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Stream processing is a new sight of processing, the business logic is
applied to every transaction that is being recorded in real-time in a system,
for example E-commerce, the Internet of Things (IofT) with various sensors that
emit data online, online monitoring of traffic in a city, telecom, banking,
etc. In other words, stream processing applies business logic to each event
that is being captured online in instead of store whole events and hence
process it as a batch. The highlight of process in this way is that the
analysis will show the real or online state of the data at this instance, that
is in real-time with unbounded data.<o:p></o:p></span></div>
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<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">5.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Batch
Processing<o:p></o:p></span></b></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Bath processing is processing a huge volume of bounded data at once.
The steps need for processing are called batch jobs. Batch jobs could be stored
up during working hours for example working hour and hence executed during the
evening, or even during weeks or months and executed on weekend or once a
month. The classic example of batch processing is how the credit card companies
process billing. The client does not receive a bill for each transaction,
usually a customer receives the billing each month when whole data has been
collected. One example for managing it is Hadoop that <span style="color: #333333;">provides
map Reduce as a processing tool for these large scale files which can be months
or years of data stored.<o:p></o:p></span></span></div>
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<a href="http://www.itrelease.com/wp-content/uploads/2012/12/Batch-processing-system.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="http://www.itrelease.com/wp-content/uploads/2012/12/Batch-processing-system.png" height="208" width="400" /></a></div>
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<span style="color: #333333; font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">[1]</span></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span>
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span>
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span>
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span>
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span>
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span>
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><br /></span></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">6.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Flink
DataStream API (for Stream Processing)<o:p></o:p></span></b></div>
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<br /></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Data Stream is the main API that offers Apache Flink, and what makes
difference with its competitors. DataStream API allows develop programs (in
Java, Scala and Python) that implement transformations on data streams (see
examples in 6.1). The data streams are initially created from multiple sources
such as message queues, socket streams or files. The results of the data
streams return via Data Sinks, which allow write the data to distributed files
or for example command line terminal.<o:p></o:p></span></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level2 lfo2; text-align: justify; text-indent: -.25in;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">6.1<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;"> </span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Examples of
transformations in Flink: <o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Map<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">FlatMap<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Filter<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">KeyBy<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Reduce<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Fold<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Aggregations<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Window<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">WindowAll<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Window Apply<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Window Reduce<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Window Fold<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Aggregations on windows<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Union<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Window Join<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Window CoGroup<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Connect<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">CoMap, CoFlatMap<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Split<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Select<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Iterate<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Extract Timestamps<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Project (for data streams of Tuples)<o:p></o:p></span></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">7.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Flink DataSet
API (for Batch Processing)<o:p></o:p></span></b></div>
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<br /></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Apache Flink provides a DataSet API that allows to developers can
develop programs (in Java, Scala and Python), that implement transformations on
data sets (see examples in 7.1). The data sets are initially created from multiple
sources such as File-Based, Collection-Based, Socket-based, Custom. The results
of the data sets return via Data Sinks, which allow write the data to
distributed files or for example command line terminal.<o:p></o:p></span></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level2 lfo2; text-align: justify; text-indent: -.25in;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">7.1<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;"> </span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Examples of
transformations in Flink:<o:p></o:p></span></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Map<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">FlatMap<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Map Partition<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Filter<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Reduce<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">ReduceGroup<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Aggregate<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Distinct<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Join<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">OuterJoin<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">CoGroup<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Cross<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Union<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Rebalance<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Hash-Partition<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Range-Partition<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Custom Partitioning<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Sort Partition<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">First-n<o:p></o:p></span></div>
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<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Project (for data streams of Tuples)<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l0 level1 lfo1; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">MinBy / MaxBy (for data streams of Tuples)<o:p></o:p></span></div>
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<br /></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">8.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">FlinkCEP –
Complex event processing for Flink<o:p></o:p></span></b></div>
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<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Apache Flink includes a complex event processing library that allows
to developers detect complex event patterns in a stream of endless data. With
the analysis of Matching sequences data scientist can construct complex events
and do a deep analysis of the data.<o:p></o:p></span></div>
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<span style="font-family: "calibri" , sans-serif; font-size: 11.0pt; line-height: 107%;"><br clear="all" style="mso-special-character: line-break; page-break-before: always;" />
</span>
</div>
<div class="MsoNormal">
<br /></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">9.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Streaming SQL<o:p></o:p></span></b></div>
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<br /></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Streaming SQL is a query language that extends the traditional SQL
capabilities to process real-time data streams. The main challenge is
incorporate aggregations, windows and time semantics on streams. Nowadays,
Apache Flink community is working with Apache Calcite community to develop a
new model for solve these challenges and improvements. <o:p></o:p></span></div>
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<br /></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">10.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Flink Table API
& SQL<o:p></o:p></span></b></div>
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<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Flink Table API and SQL are experimental features focusing in
Streaming SQL that allow to work with SQL-like expressions for relational
stream and batch processing. The Table API and SQL interface operate on a
relational Table abstraction and provide tight integration with Flink DataSet
API.<o:p></o:p></span></div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">11.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Flink ML
(Machine Learning)<o:p></o:p></span></b></div>
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<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Apache Flink provides an extensive and newly scalable Machine
Learning (ML) library for Flink developers, the main two goals of Flink ML are
to help to developers keep glue code to a minimum and second goal is to make it
easy to use providing detailed documentation with examples. Flink ML allows to
data scientist to test their algorithms and models locally with a subset of the
total data and hence with the same written code this can be executed at a much
larger scale in a cluster setting.<o:p></o:p></span></div>
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<br /></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">The Machine learning algorithms supported at the moment are:<o:p></o:p></span></div>
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<br /></div>
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<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Supervised Learning<o:p></o:p></span></b></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">SVM using
Communication efficient distributed dual coordinate ascent (CoCoA)<o:p></o:p></span></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Multiple linear
regression<o:p></o:p></span></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Optimization
Framework<o:p></o:p></span></div>
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<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Data Preprocessing<o:p></o:p></span></b></div>
<div class="Standard" style="margin-left: 1.0in; text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Polynomial
Features<o:p></o:p></span></div>
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<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Standard Scaler<o:p></o:p></span></div>
<div class="Standard" style="margin-left: 1.0in; text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">MinMax Scaler<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; text-align: justify;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Recommendation<o:p></o:p></span></b></div>
<div class="Standard" style="margin-left: .5in; text-align: justify; text-indent: .5in;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Alternating
Least Squares (ALS)<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; text-align: justify;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Utilities<o:p></o:p></span></b></div>
<div class="Standard" style="margin-left: .5in; text-align: justify; text-indent: .25in;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Distance
Metrics<o:p></o:p></span></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; font-size: 11.0pt; line-height: 107%;"><br clear="all" style="mso-special-character: line-break; page-break-before: always;" />
</span>
</div>
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<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">12.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; font-weight: normal; line-height: normal;">
</span></span></b><!--[endif]--><b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Flink Gelly
(Graph Processing)<o:p></o:p></span></b></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Gelly is a Graph API for Flink that contains variety of methods and
tool (such as graph transformations and utilities, iterative graph processing,
library of graph algorithms) for doing graph analysis applications in Flink.
Gelly can be seamlessly mixed with the DataSet Flink API for developing
programs that use both record-based and graph-based analysis.<o:p></o:p></span></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Flink Gelly provide the next Graph Methods:<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l1 level1 lfo3; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Graph Properties (e.g. getVertexIds, getEdgelds, numberOfVerices,
numberOfEdgest, etc)<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l1 level1 lfo3; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Transformations (e.g. map, filter, join, subgraph, union, difference,
reverse, undirected, getTriplets, etc.)<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l1 level1 lfo3; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Mutations (e.g. add vertex, add edge, remove vertex, remove edge)<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l1 level1 lfo3; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Neighborhood Methods.<o:p></o:p></span></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Some Algorithms provides in Flink Gelly:<o:p></o:p></span></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l2 level1 lfo4; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">PageRank<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l2 level1 lfo4; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Single Source Shortest Paths<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l2 level1 lfo4; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Label Propagation<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l2 level1 lfo4; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Weakly Connected Components<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l2 level1 lfo4; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Community Detection<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l2 level1 lfo4; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Triangle Count & Enumeration<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; mso-list: l2 level1 lfo4; text-align: justify; text-indent: -.25in;">
<!--[if !supportLists]--><span style="font-family: "symbol"; mso-bidi-font-family: Symbol; mso-fareast-font-family: Symbol;">·<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Graph Summarization<o:p></o:p></span></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="margin-left: .5in; mso-list: l3 level1 lfo2; text-align: justify; text-indent: -.25in;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin;">13.<span style="font-family: "times new roman"; font-size: 7pt; font-stretch: normal; line-height: normal;">
</span></span><!--[endif]--><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">BONUS term<o:p></o:p></span></div>
<div class="Standard" style="margin-left: .5in; text-align: justify;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Blink (Alibaba Flink)<o:p></o:p></span></b></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Blink is a project (improvements to Flink) from Alibaba Group, which
operates the world’s largest online marketplace and it profits bigger than
Amazon and eBay combined. These improvements include better changes in Flink
Table API (such as unification of SQL layer for batch and streaming, adding
features stream-stream join, aggregations, windowing, retraction) and Runtime
Compatibility with Flink API and Ecosystem (such as new runtime architecture on
YARN, optimized state, checkpoint & failover, reliable and production
quality and others). <o:p></o:p></span></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
<b><span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Summary<o:p></o:p></span></b></div>
<div class="Standard" style="text-align: justify;">
<br /></div>
<div class="Standard" style="text-align: justify;">
</div>
<div class="Standard" style="text-align: justify;">
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">Apache Flink could be considering as the 4th generation of Big Data
Framework, instead of waiting for a long cycle of batch processing until data
could be available, data scientist can work in real-time with data generated
and processed continuously.</span><br />
<br />
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">References:</span><br />
<br />
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;">[1]<span style="font-family: "calibri" , sans-serif;"> <span style="font-family: "calibri" , sans-serif;"><span style="font-family: "calibri" , sans-serif;">Batch Processing, http://www.itrelease.com/2012/12/what-are-advantages-and-disadvantages-of-batch-<br />processing-systems/ </span></span></span> </span><br />
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"></span><br />
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"></span><br />
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"></span><br />
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"></span><br />
<span style="font-family: "calibri" , sans-serif; mso-ascii-theme-font: minor-latin; mso-bidi-font-family: FreeSans; mso-hansi-theme-font: minor-latin;"><o:p></o:p></span></div>
AVhttp://www.blogger.com/profile/11395612248433948335noreply@blogger.com0Berlin, Germany52.520006599999988 13.40495399999997552.210736099999991 12.759506999999974 52.829277099999985 14.050400999999976tag:blogger.com,1999:blog-7332477090953898587.post-50508036711739596742016-08-16T17:12:00.000+02:002016-08-16T17:12:01.375+02:00IT4BI becomes BDMA!<p>The Master's Degree in <b>Information Technologies for Business Intelligence</b> (<b>IT4BI</b>) received its <i>first generation</i> of students in September 2012. One year later, the <i>second generation</i> of which I was part, would be welcomed to start studies in Brussels as well.</p>
<p>Since its beginning, IT4BI has been facilitating <b>learning</b>, <b>research </b>and <b>international collaboration</b> in a <b>positive </b>and <b>friendly atmosphere</b>, thus providing the means for personal and professional development of many people around the World.</p>
<p>By the end of September 2016, the <i>fifth generation</i> of IT4BI students will have started their studies in Brussels while the <i>third generation</i> completes their thesis defences and the <i>fourth generation</i> begins their specialisation semesters in Barcelona, Berlin and Paris.</p>
<p>In September 2017, IT4BI will become <b>BDMA</b>, which stands for "<b>Big Data Management and Analytics</b>". BDMA focuses on the new needs of research, education, and industry with respect to Big Data and will keep on receiving the support of the European Commission as part of the Erasmus+ Programme.</p>
<p>IT4BI has literally changed many lives and there are no doubts that BDMA will keep on doing so. You may learn more about BDMA programme on the <a href="http://bdma.univ-tours.fr/bdma">official website</a>.</p>IT Sharedhttp://www.blogger.com/profile/11763288513127086508noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-55648598543704600492015-12-26T21:31:00.000+01:002015-12-26T21:31:04.154+01:00Codeforces Submissions: Dataset for Source Code Analysis<h1 id="codeforces-submissions-dataset">Codeforces Submissions Dataset</h1>
<p></p><center><img src="https://habrastorage.org/files/da5/846/791/da58467913ba42dfb52ee66cbf6c4d0e.png" style="max-height: 100%; max-width: 100%;"></center><p></p>
<p>I wanted to do some analysis on source code, and I needed a dataset where code snippets are labeled with the programming language they are in. I scraped this data from <a href="http://codeforces.com/?locale=en">codeforces.com</a>, which is a website for holding programming <a href="http://codeforces.com/contests">contests</a>. In this post, I share this data.</p>
<p><strong>tl;dr</strong> Scroll down to get the links.</p>
<a name='more'></a>
<h2 id="codeforces">Codeforces</h2>
<p>On <a href="http://codeforces.com">codeforces.com</a> participants are given some <a href="http://codeforces.com/problemset">programming problems</a>, and they need to solve them using any language the platform supports. It’s possible to solve the problems using a lot of languages, including C/C++, Java, Python and others. <a href="http://codeforces.com">Codeforces.com</a> is a very good website for whose who want to lean algorithms and programming: not only the problems are fun to solve, but also you can see how the problems were solved by others – that is, you can have a look at the source code of each submission to the platform. </p>
<p>It is also interesting for data analysis: each submission has some <a href="http://codeforces.com/problemset/status">metadata</a> with the source code, including the language used and the verdict (accepted/not accepted/compilation error). So it seemed like the best choice to get the dataset I needed.</p>
<h2 id="scraping">Scraping</h2>
<p>I didn’t find direct links to get the source code for each submission. Codeforces has some <a href="http://codeforces.com/api/help">API</a>, but for some reasons I wasn’t able to use it to get the source code. </p>
<p>The only way to get the code was by using the website: after clicking on a submission, and it shows the code in a pop-up. Thus, to get the data I needed to emulate a web browser. For that wrote a small script in Java using <a href="http://www.seleniumhq.org/">Selenium</a>, and you can see it here: <a href="https://github.com/alexeygrigorev/projects/blob/master/codeforces-crawl/src/main/java/com/alexeygrigorev/codeforcescrawl/CodeExtractor.java">CodeExtractor.java</a>. Selenium is quite slow, so I used multi-threading to scrape data in parallel. Then the data was put to a MySQL database. </p>
<p>I extracted the following data: </p>
<ul>
<li><code>submission_id</code> - the ID of the individual submission</li>
<li><code>source</code> - the source code of the submission</li>
<li><code>status</code> - the verdict of the submission (accepted, not accepted, compilation error, etc)</li>
<li><code>language</code> - the programming language which was used to make the submission</li>
<li><code>problem</code> - the ID of the problem</li>
</ul>
<p>You can download it here:</p>
<ul>
<li><a href="https://www.dropbox.com/s/q3v4pdi9vy5og9g/dump.sql.gz">https://www.dropbox.com/s/q3v4pdi9vy5og9g/dump.sql.gz</a></li>
<li>mirror: <a href="https://yadi.sk/d/9yGx3a2XmWUDu">https://yadi.sk/d/9yGx3a2XmWUDu</a></li>
</ul>
<p>This is a MySQL dump, so you need MySQL to import this dataset.</p>
<h2 id="tokenization">Tokenization</h2>
<p>The raw source code is hard to work with, and to do something meaningful we first need to make it machine-processable. The easiest way to do it is to tokenize it – i.e. split the source code into tokens. For example, consider the following snippet in Python:</p>
<pre><code>def greet(name):
print 'Hello', name
greet('Jack')
</code></pre>
<p>It can be represented as this list of tokens <code>["def", "greet", "(", "name", ")", ":", "print", "'Hello'", ",", "name", "greet", "(", "'Jack'", ")"]</code>.</p>
<p>This representation is easier for analysis of this dataset. </p>
<p>For tokenization I used <a href="https://docs.oracle.com/javase/8/docs/api/java/io/StreamTokenizer.html"><code>StreamTokenizer</code></a> from Java. It is pretty generic and can handle other programming languages, not just Java or syntactically similar languages (e.g. C/C++): it does a good job tokenizing Pascal, Python, Ruby, Haskell and others. You can see the code of the tokenizer here: <a href="https://github.com/alexeygrigorev/projects/blob/master/codeforces-crawl/src/main/java/com/alexeygrigorev/codeforcescrawl/BowFeatureExtractor.java">BowFeatureExtractor.java</a>.</p>
<p>I already pre-tokenized the dataset, and you can download it here:</p>
<ul>
<li><a href="https://www.dropbox.com/s/6fop7qq0em9x279/out.json.gz">https://www.dropbox.com/s/6fop7qq0em9x279/out.json.gz</a></li>
<li>mirror: <a href="https://yadi.sk/d/0q9A6cN3mWUuw">https://yadi.sk/d/0q9A6cN3mWUuw</a></li>
</ul>
<p>(It may say that the checksum does not match for the, just ignore it – for some reason Java doesn’t gzip very well.)</p>
<p>You don’t need MySQL to use this version of the dataset.</p>
<h2 id="some-statistics">Some statistics</h2>
<p>There are about 270k submissions in the dataset and C/C++ is the most popular choice (more than 90% of all submissions are C/C++):</p>
<p></p><center><img src="https://habrastorage.org/files/f0b/52c/1da/f0b52c1daa874db4bd9a780073b9b00e.png" alt="" title=""></center><p></p>
<p>Note that this is a very tiny subset of all codeforces submissions!</p>
<h2 id="example">Example</h2>
<p>To see how you can use this dataset, I prepared a Jupyter notebook, where I build a simple Decision Tree model to determine the language of a submission. </p>
<p>You can see it here: <a href="http://nbviewer.ipython.org/github/alexeygrigorev/notebooks/blob/master/analysis/codeforces-langdetect.ipynb">codeforces-langdetect.ipynb</a>.</p>
<p>First, I classify <code>C/C++</code> vs not <code>C/C++</code>, and then I build a model to classify the rest. I don’t use all the tokens form the source code, only the keywords. The list of keywords is taken form <a href="https://notepad-plus-plus.org/">Notepad++</a> – they are kept in a file called <code>langs.model.xml</code>. </p>
<p>Here’s the model I get for <code>C/C++</code> vs the rest:</p>
<pre><code>if contains('std'):
if contains('import'):
if contains('scan'):
return 'C/C++' # (1.000, 3/3 examples)
else: # doesn't contain 'scan'
return 'OTHER' # (1.000, 47/47 examples)
else: # doesn't contain 'import'
if contains('String'):
return 'C/C++' # (0.800, 4/5 examples)
else: # doesn't contain 'String'
return 'C/C++' # (0.999, 230230/230376 examples)
else: # doesn't contain 'std'
if contains('printf'):
if contains('String'):
return 'OTHER' # (0.992, 604/609 examples)
else: # doesn't contain 'String'
return 'C/C++' # (0.993, 12940/13029 examples)
else: # doesn't contain 'printf'
if contains('#include'):
return 'C/C++' # (1.000, 193/193 examples)
else: # doesn't contain '#include'
return 'OTHER' # (0.977, 28517/29181 examples)
</code></pre>
<p>You can see the other model for the rest of the languages in the notebook. </p>
<p>I hope you’ll find this dataset useful. If you do, and you run some interesting analysis, please let us know the results. </p>
<p>Stay tuned! </p>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-45673756406374075922015-12-26T00:42:00.000+01:002015-12-26T00:42:54.484+01:00Business Intelligence in the Non-Profit Sector<p>Beyond any shadow of a doubt, a sufficient amount of <b>correct</b>, <b>relevant</b>, <b>concise</b> and <b>up-to-date information</b> is a key input in any <b>decision-making</b> process. This not only applies to profit-driven organisations but it is also relevant for the non-profit sector.</p>
<p>For instance, in a non-profit organisation, having access to <b>membership information</b> of good quality and in an efficient way is of utmost importance at the moment of defining membership <b>strategies</b>. Furthermore, good information is also crucial when it comes to translating strategies into <b>tactics</b> and, subsequently, turning the latter into action on the <b>operational</b> landscape.</p>
<center><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8LpwSOOF8L9bY6Zlrs5eoi24oxhDAf-k9iG2m9lZeQCyR4hT-I02joIP_JcMBCzLtTZvwLPcSAWYYUFYjdu8S7hAwadciCBxQDwm4XSv0MeW5wmQHhMjOCBZVWMR-t2KGqOVxuNr__1Nb/s1600/PMI+Report+Sample.png" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi8LpwSOOF8L9bY6Zlrs5eoi24oxhDAf-k9iG2m9lZeQCyR4hT-I02joIP_JcMBCzLtTZvwLPcSAWYYUFYjdu8S7hAwadciCBxQDwm4XSv0MeW5wmQHhMjOCBZVWMR-t2KGqOVxuNr__1Nb/s500/PMI+Report+Sample.png" /></a></center>
<a name='more'></a>
<p>Let us discuss a real-world case. The <b><i>Project Management Institute</i></b> (<i>PMI</i>) is a not-for-profit professional membership association founded in 1969 for the project, program and portfolio management profession. It is headquartered in Pennsylvania, United States and it has almost 300 local Chapters all over the World.</p>
<p>I joined PMI in 2012, while I was preparing for the PMP certification exam. Firstly, I participated in conferences in <b><i>PMI Buenos Aires Chapter</i></b> and, when I moved to Paris to attend the second year of IT4BI Master's Degree studies at <i>École Centrale Paris</i>, I joined <b><i>PMI France Chapter</i></b> and I started working with them as a volunteer.</p>
<p>PMI Chapters use a system called "CRS", which provides <b>detailed information</b> about their current and prospective members. This includes not only contact data but also details about the fulfilment of the continuous learning and growth requirements set forth by the PMI certifications programme.</p>
<p>Notwithstanding, the required level of <b>information granularity</b> undoubtedly changes at each <b>organisational level</b>. Detailed information is useful on the operational level, but tactical and strategical levels certainly require more <b>aggregated</b> information. Most importantly, from a <b>Data Quality</b> perspective, accuracy, consistency, completeness and uniqueness are always aspects that pose opportunities for improvement in any transactional or analytical database.</p>
<p>With those ideas in mind, we gave birth to the <b>Membership Management System</b>, trying to take advantage of the benefits that Business Intelligence has been delivering for years in different types of organisations all over the World.</p>
<p>In particular, our system makes use of "Extract, Transformation and Load" (ETL) processes to <b>clean</b>, <b>homogenise</b> and <b>enrich</b> the information that originally comes from the CRS system. The refined information may later be explored in a <b>wide variety of analytical ways</b>, thus setting the grounds for delivering the strategical, tactical and operational information that any PMI Chapter needs as far as membership is concerned. This is done in a <b>convenient "self-service" way</b> where users connect to a <b>centralised server</b> instead of exchanging files, posing advantages regarding information <b>freshness</b>, <b>security</b> and <b>efficiency</b>.</p>
<p>Some of the <b>key features</b> and <b>benefits</b> of the system include, but are not limited to:</p>
<ul>
<li>Visualisation of data in both <b>detailed</b> and <b>aggregated</b> ways.</li>
<li>Possibility of assigning members according to the Chapter’s <b>regionalisation</b> strategy (e.g. branches).</li>
<li>Display of <b>targeted data</b> according to each user’s needs, filtering by company and branch if necessary.</li>
<li><b>History tracking</b> of the evolution of personal and certifications data.</li>
<li><b>Easy navigation</b> through categories such as "PMI only members", "Chapter members", "Non-members", "Joined last month", "Certified last month", "Expired this month", "PMP", etc.</li>
<li><b>Enrichment</b> of personal data with certifications data.</li>
<li>Calculation of <b>derived fields</b> (e.g. Industry, PM Professional level, etc.).</li>
<li><b>General corrections</b> (e.g. renaming a company after a merger).</li>
<li><b>Individual corrections</b> (e.g. changing the affiliation data of a person when CRS data is outdated).</li>
<li><b>Merging data</b> from current and prospect members.</li>
<li><b>Standard capitalisation</b> of names, surnames, countries and addresses.</li>
<li><b>Splitting</b> of merged fields.</li>
<li>Possibility of <b>flagging</b> people who have expressed they would not like to be contacted.</li>
</ul>
<p>You may have a look at the design of the solution in <a href="https://db.tt/Yv9SdiFg">this document</a>.</p>
<p>To sum it up, this is not a rocket science technology and there is still a lot of work to be done in order to be able to take advantage of the maximum potential of the solution. However, this is an example of how <b>Business Intelligence can also help non-profit organisations</b>.</p>
<p>The server-side part of the system we developed is totally open source and it is based on free software tools like <b>Talend ETL</b> and <b>MySQL database</b> server. As a result, <b>it does not imply any licencing fees</b>. The only requirements for the implementation is to have a server with network access to install the solution and to have spreadsheet software available on the end-users' computers.</p>
<p>We are aware that the solution not only covers the needs we found in PMI France but it could definitely be considered as a great asset by <b>other international chapters</b> and we are in the process of sharing it. We are currently implementing it in <b>PMI France</b> and in <b>PMI Montreal</b>. We are also collaborating with a private company in the United States who have shown their interest for potentially extending this system to some of the other 120 PMI Chapters they serve.</p>
<p>Do you have any story of <b>Business Intelligence</b> in the <b>Non-Profit Sector</b>? Feel free to <b>share</b> it!</p>IT Sharedhttp://www.blogger.com/profile/11763288513127086508noreply@blogger.com0Saint-Louis, France47.5859949 7.563934000000017547.5859949 7.5639340000000175 47.5859949 7.5639340000000175tag:blogger.com,1999:blog-7332477090953898587.post-83011842887439979812015-12-17T11:04:00.001+01:002015-12-17T13:40:02.151+01:00Test-Driven Machine Learning<h2 id="test-driven-machine-learning">Test-Driven Machine Learning</h2>
<center><img src="https://habrastorage.org/files/001/cf3/a5c/001cf3a5c6dc4b97a6f88df6fb91dfc2.jpg" border="0" style="max-height: 100%; max-width: 100%;"></center>
<p>The book “Test-Driven Machine Learning” by Justin Bozonier, published by Packt Publishing, is in print now. I was a technical reviewer of this book, and in this post you will learn some details about it. The book is available on the <a href="http://bit.ly/1T3qcnf">publisher’s website</a> as well as on <a href="https://www.safaribooksonline.com/library/view/test-driven-machine-learning/9781784399085/">Safari Books Library</a>.</p>
<a name='more'></a>
<p>If you are a software developer with experience in TDD, and you’re interested in Data Science and Machine Learning, this is a very good book for you. But also, if you’re a data scientist who’s interested in learning how to apply the best practices of software development to Data Science work, this is an excellent resource. </p>
<p>The book uses python, so some familiarity with the language will be useful to get the most out of the book.</p>
<br/>
<p><strong>Table of Contents</strong></p>
<ol>
<li>Introducing Test-Driven Machine Learning</li>
<li>Perceptively Testing a Perceptron</li>
<li>Exploring the Unknown with Multi-armed Bandits</li>
<li>Predicting Values with Regression</li>
<li>Making Decisions Black and White with Logistic Regression</li>
<li>You’re So Naïve, Bayes</li>
<li>Optimizing by Choosing a New Algorithm</li>
<li>Exploring scikit-learn Test First</li>
<li>Bringing It All Together</li>
</ol>
<p><strong>What You Will Learn:</strong></p>
<ul>
<li>Get started with an introduction to test-driven development and familiarize yourself with how to apply these concepts to machine learning</li>
<li>Build and test a neural network deterministically, and learn to look for niche cases that cause odd model behaviour</li>
<li>Learn to use the multi-armed bandit algorithm to make optimal choices in the face of an enormous amount of uncertainty</li>
<li>Generate complex and simple random data to create a wide variety of test cases that can be codified into tests</li>
<li>Develop models iteratively, even when using a third-party library</li>
<li>Quantify model quality to enable collaboration and rapid iteration</li>
<li>Adopt simpler approaches to common machine learning algorithms</li>
<li>Take behaviour-driven development principles to articulate test intent</li>
</ul>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-16335823966504855612015-10-19T07:29:00.000+02:002016-06-09T13:08:31.390+02:00Data Science Interview Questions<h1 id="data-science-interview-questions">Data Science Interview Questions</h1>
<p></p><center><img src="https://habrastorage.org/files/028/268/de2/028268de2fed443c8876ac607cd36bd5.png"><br>
<small>Source: <a href="https://en.wikibooks.org/wiki/Data_Science:_An_Introduction/A_Mash-up_of_Disciplines">Data Science: An Introduction</a></small></center><p></p>
<p>Our <a href="http://www.itshared.org/search/label/IT4BI">IT4BI Master studies</a> finished, and the next logical step after graduation is finding a job. I was interested in Data Science jobs and this post is a summary of my interview experience and preparation.</p>
<p>The term “Data Science” is not yet well establish, so interviews for Data Science jobs might include a very broad range of questions, depending on the interpretation of the term by a particular company. In this post I attempt to organize Data Science interview questions in some usable form, but it might also be biased by how I see Data Science myself. I hope you also can find it useful.</p>
<a name='more'></a>
<p>The sources of the questions are:</p>
<ul>
<li>links that I discovered on the Internet,</li>
<li>my own data science interviews (being on the interviewee side)</li>
</ul>
<p>The questions are without answers. First of all, the answer that I would write could be bad or wrong, and second, the post would be too big. Also, going through the list and looking for the answers yourself is a good exercise to prepare for an interview.</p>
<p>This list might look scary at first, but it’s very unlikely that all of these questions will be asked during one interview. Very few jobs require applicants to know all of these points. So it’s rather a broad overview of things that may potentially be asked. Don’t let this list of questions discourage you if you don’t know the answer to some of them: chances are that these questions are not important for your interview.</p>
<p>So, let’s get started.</p>
<h2 id="table-of-content">Table of Content</h2>
<!-- [TOC] -->
<ul>
<li><a href="#background-questions">Background Questions</a></li>
<li><a href="#process">Process</a></li>
<li><a href="#mathematics">Mathematics</a><ul>
<li><a href="#linear-algebra">Linear Algebra</a></li>
<li><a href="#other-areas">Other Areas</a></li></ul></li>
<li><a href="#probability-and-statistics">Probability and Statistics</a><ul>
<li><a href="#basic-probability">Basic Probability</a></li>
<li><a href="#distributions">Distributions</a></li>
<li><a href="#basic-statistics">Basic Statistics</a></li>
<li><a href="#experiment-design">Experiment Design</a></li>
<li><a href="#point-estimates">Point Estimates</a></li>
<li><a href="#testing">Testing</a></li>
<li><a href="#ab-tests">A/B Tests</a></li>
<li><a href="#bayesian-statistics">Bayesian Statistics</a></li>
<li><a href="#time-series">Time Series</a></li>
<li><a href="#advanced">Advanced</a></li></ul></li>
<li><a href="#machine-learning">Machine Learning</a><ul>
<li><a href="#general-ml-questions">General ML Questions</a></li>
<li><a href="#regression">Regression</a></li>
<li><a href="#classification">Classification</a></li>
<li><a href="#regularization">Regularization</a></li>
<li><a href="#dimensionality-reduction">Dimensionality Reduction</a></li>
<li><a href="#cluster-analysis">Cluster Analysis</a></li>
<li><a href="#optimization">Optimization</a></li>
<li><a href="#recommendation">Recommendation</a></li>
<li><a href="#feature-engineering">Feature Engineering</a></li>
<li><a href="#natural-language-processing">Natural Language Processing</a></li>
<li><a href="#meta-learning">Meta Learning</a></li>
<li><a href="#miscellanea">Miscellanea</a></li></ul></li>
<li><a href="#computer-science">Computer Science</a><ul>
<li><a href="#libraries-and-tools">Libraries and Tools</a></li>
<li><a href="#databases">Databases</a></li>
<li><a href="#distributed-systems-and-big-data">Distributed Systems and Big Data</a></li></ul></li>
<li><a href="#hands-on">Hands-On</a><ul>
<li><a href="#problem-to-solve">Problem to Solve</a></li>
<li><a href="#coding">Coding</a></li>
</ul></li>
<li><a href="#sources">Sources</a></li>
<li><a href="#useful-links">Useful Links</a></li></ul>
<p> </p>
<h2 id="background-questions">Background Questions</h2>
<p>Usually, interviews start with background questions: they can ask you to talk about yourself. This can also happen at the telephone interview stage.</p>
<p>For background questions be ready to talk about a summary of your career.</p>
<ul>
<li>Summarize your experience</li>
<li>What companies you worked at? What was your role?</li>
<li>Do you have a project portfolio? What projects you implemented? Discuss some of them in details</li>
<li>For graduating students: Tell me about your master thesis</li>
<li>For aspiring data scientists: Why do you want a career in data science?</li>
<li>Have you taken any data-science-related online courses? If yes, how many did you complete with a certificate?</li>
<li>Have you participated in any data science challenges? If yes, can you describe one of them? </li>
</ul>
<p> </p>
<h2 id="process">Process</h2>
<p>All Machine Learning, Data Mining and Data Science projects should follow some process, so there can be questions about it:</p>
<ul>
<li>Can you outline the steps in a data science project?</li>
<li>Have you heard of CRISP-DM (Cross Industry Standard Process for Data Mining)?</li>
</ul>
<p>CRISP-DM defines the following steps:</p>
<ul>
<li>Problem Definition</li>
<li>Data Understanding (or Data Exploration)</li>
<li>Data Preparation</li>
<li>Modeling</li>
<li>Evaluation</li>
<li>Deployment (for the production)</li>
</ul>
<p>So next you may discuss each of these steps in details</p>
<ul>
<li>What is the goal of each step?</li>
<li>What are possible activities at each step?</li>
</ul>
<p> </p>
<h2 id="mathematics">Mathematics</h2>
<p>Some background mathematics is necessary for doing Data Science, therefore you may expect math-related questions. On the other hand, for some Data Science positions there could be very few math questions, or none at all. In my opinion, it's always better to know the underlying theory when talking about Machine Learning algorithms, but your interviewers may have a different point of view.</p>
<p> </p>
<h3 id="linear-algebra">Linear Algebra</h3>
<p>Basic Linear Algebra questions might include:</p>
<ul>
<li>What is <script id="MathJax-Element-20" type="math/tex">A \mathbf x = \mathbf b</script>? How to solve it?</li>
<li>How do we multiply matrices?</li>
<li>What is an Eigenvalue? And what is an Eigenvector? What is Eigenvalue Decomposition or The Spectral Theorem?</li>
<li>What is Singular Value Decomposition?</li>
<li>You may expect Liner Algebra questions in the Machine Learning part of the interview (see below).</li>
</ul>
<p>If you are interested in learning or refreshing Linear Algebra, see <a href="http://www.itshared.org/2015/02/best-time-to-learn-linear-algebra-is-now.html">Best Time to Learn Linear Algebra is Now!</a></p>
<p> </p>
<h3 id="other-areas">Other Areas</h3>
<ul>
<li>Discrete Mathematics and Logics are not that important for Data Science</li>
<li>Probability and Statistics are core skills and discussed in the next section</li>
<li>Calculus and Optimization are usually discussed in the Machine Learning part and usually when talking about a particular algorithm</li>
</ul>
<p> </p>
<h2 id="probability-and-statistics">Probability and Statistics</h2>
<p>Probability and Statistics give the foundation for Machine Learning, which makes them an important subject. It also may be useful if the company is doing some marketing or website optimization, so they could ask about related concepts such as A/B tests.</p>
<p> </p>
<h3 id="basic-probability">Basic Probability</h3>
<p>You can have a couple of simple questions to check your understanding of probability.</p>
<p>For example:</p>
<ul>
<li>Given two fair dices, what is the probability of getting scores that sum to 4? to 8?</li>
<li>A simple questions on Bayes rule: Imagine a test with a true positive rate of 100% and false positive rate of 5%. Imagine a population with a 1/1000 rate of having the condition the test identifies. Given a positive test, what is the probability of having that condition?</li>
</ul>
<p> </p>
<h3 id="distributions">Distributions</h3>
<p>You may expect questions about probability distributions:</p>
<ul>
<li>What is the normal distribution? Give an example of some variable that follows this distribution</li>
<li>What about log-normal?</li>
<li>Explain what a long tailed distribution is and provide three examples of relevant phenomena that have long tails. Why are they important in classification and prediction problems?</li>
<li>How to check if a distribution is close to Normal? Why would you want to check it? What is a QQ Plot? </li>
<li>Give examples of data that does not have a Gaussian distribution, or log-normal. </li>
<li>Do you know what the exponential family is?</li>
<li>Do you know the Dirichlet distribution? the multinomial distribution?</li>
</ul>
<p> </p>
<h3 id="basic-statistics">Basic Statistics</h3>
<ul>
<li>What is the Laws of Large Numbers? Central Limit Theorem?</li>
<li>Why are they important for Statistics?</li>
<li>What summary statistics do you know?</li>
</ul>
<p> </p>
<h3 id="experiment-design">Experiment Design</h3>
<p>Designing experiments is an important part of Statistics, and it’s especially useful for doing A/B tests. </p>
<p>Sampling and Randomization</p>
<ul>
<li>Why do we need to sample and how? </li>
<li>Why is randomization important in experimental design?</li>
<li>Some 3rd party organization randomly assigned people to control and experiment groups. How can you verify that the assignment truly was random?</li>
<li>How do you calculate needed sample size? </li>
<li>Power analysis. What is it? </li>
</ul>
<p>Biases</p>
<ul>
<li>When you sample, what bias are you inflicting?</li>
<li>How do you control for biases?</li>
<li>What are some of the first things that come to mind when I do X in terms of biasing your data?</li>
</ul>
<p>Other questions</p>
<ul>
<li>What are confounding variables? </li>
</ul>
<p> </p>
<h3 id="point-estimates">Point Estimates</h3>
<p>Confidence intervals</p>
<ul>
<li>What is a point estimate? What is a confidence interval for it?</li>
<li>How are they constructed?</li>
<li>How to interpret confidence intervals?</li>
</ul>
<p> </p>
<h3 id="testing">Testing</h3>
<p>Hypothesis tests</p>
<ul>
<li>Why do we need hypothesis testing? What is P-Value?</li>
<li>What is the null hypothesis? How do we state it? </li>
<li>Do you know what Type-I/Type-II errors are?</li>
<li>What is <script id="MathJax-Element-21" type="math/tex">t</script>-Test/<script id="MathJax-Element-22" type="math/tex">F</script>-Test/ANOVA? When to use it? </li>
<li>How would you test if two populations have the same mean? What if you have 3 or 4 populations?</li>
<li>You applied ANOVA and it says that the means are different. How do you identify the populations where the differences are significant? </li>
<li>What is the distribution of p-value’s, in general?</li>
</ul>
<p> </p>
<h3 id="ab-tests">A/B Tests</h3>
<ul>
<li>What is A/B testing? How is it different from usual Hypothesis testing? </li>
<li>How can you prove that one improvement you’ve brought to an algorithm is really an improvement over not doing anything? How familiar are you with A/B testing? </li>
<li>How can we tell whether our website is improving?</li>
<li>What are the metrics to evaluate a website? A search engine? </li>
<li>What kind of metrics would you track for you music streaming website?</li>
<li>Common metrics: Engagement / retention rate, conversion, similar products / duplicates matching, how to measure them.</li>
<li>Real-life numbers and intuition: Expected user behavior, reasonable ranges for user signup / retention rate, session length / count, registered / unregistered users, deep / top-level engagement, spam rate, complaint rate, ads efficiency.</li>
</ul>
<p> </p>
<h3 id="time-series">Time Series</h3>
<ul>
<li>What is a time series? </li>
<li>Did you do any projects which involved dealing with time?</li>
<li>What is the difference between data for usual statistical analysis and time series data? </li>
<li>Have you used any of the following: Time series models, Cross-correlations with time lags, Correlograms, Spectral analysis, Signal processing and filtering techniques? If yes, in which context?</li>
<li>In time series modeling how can we deal with multiple types of seasonality like weekly and yearly seasonality?</li>
</ul>
<p> </p>
<h3 id="advanced">Advanced</h3>
<p>Resampling </p>
<ul>
<li>Explain what resampling methods are. Why they are useful. What are their limitations?</li>
<li>Bootstrapping - how and why it is used? </li>
<li>How to use resampling for hypothesis testing? Have you heard of Permutation Tests? </li>
<li>How would you apply resampling to time series data? </li>
</ul>
<p> </p>
<h2 id="machine-learning">Machine Learning</h2>
<p>In my experience, the Machine Learning part is usually the largest part of the interview. It may be a few basic questions, but it’s helpful to be prepared to more in-depth Machine Learning questions, especially if you claim to have worked with it on your CV. </p>
<h3 id="general-ml-questions">General ML Questions</h3>
<p>The ML part may start with something like:</p>
<ul>
<li>What is the difference between supervised and unsupervised learning? Which algorithms are supervised learning and which are not? Why? </li>
<li>What is your favorite ML algorithm and why? </li>
</ul>
<p>And then go into details</p>
<p> </p>
<h3 id="regression">Regression</h3>
<ul>
<li>Describe the regression problem. Is it supervised learning? Why? </li>
<li>What is linear regression? Why is it called linear? </li>
<li>Discuss the bias-variance tradeoff.</li>
</ul>
<p>Linear Regression:</p>
<ul>
<li>What is Ordinary Least Squares Regression? How it can be learned? </li>
<li>Can you derive the OLS Regression formula? (For one-step solution)</li>
<li>Is model <script id="MathJax-Element-23" type="math/tex">Y \sim X_1 + X_2 + X_1 \, X_2</script> still linear? Why? </li>
<li>Do we always need the intercept term? When do we need it and when do we not? </li>
<li>What is collinearity and what to do with it? How to remove multicollinearity? </li>
<li>What if the design matrix is not full rank? </li>
<li>What is overfitting a regression model? What are ways to avoid it?</li>
<li>What is Ridge Regression? How is it different from OLS Regression? Why do we need it? </li>
<li>What is Lasso regression? How is it different from OLS and Ridge? </li>
</ul>
<p>Linear Regression assumptions:</p>
<ul>
<li>What are the assumptions required for linear regression?</li>
<li>What if some of these assumptions are violated? </li>
</ul>
<p>Significant features in Regression</p>
<ul>
<li>You would like to find significant features. How would you do that? </li>
<li>You fit a multiple regression to examine the effect of a particular feature. The feature comes back insignificant, but you believe it is significant. Why can it happen? </li>
<li>Your model considers the feature <script id="MathJax-Element-24" type="math/tex">X</script> significant, and <script id="MathJax-Element-25" type="math/tex">Z</script> is not, but you expected the opposite result. Why can it happen?</li>
</ul>
<p>Evaluation</p>
<ul>
<li>How to check is the regression model fits the data well? </li>
</ul>
<p>Other algorithms for regression</p>
<ul>
<li>Decision trees for regression</li>
<li><script id="MathJax-Element-26" type="math/tex">k</script>-Nearest Neighbors for regression. When to use? </li>
<li>Do you know others? E.g. Splines? LOESS/LOWESS? </li>
</ul>
<p> </p>
<h3 id="classification">Classification</h3>
<p>Basic:</p>
<ul>
<li>Can you describe what is the classification problem? </li>
<li>What is the simplest classification algorithm?</li>
<li>What classification algorithms do you know? Which one you like the most? </li>
</ul>
<p>Decision trees:</p>
<ul>
<li>What is a decision tree? </li>
<li>What are some business reasons you might want to use a decision tree model?</li>
<li>How do you build it? What impurity measures do you know? </li>
<li>Describe some of the different splitting rules used by different decision tree algorithms.</li>
<li>Is a big brushy tree always good? Why would you want to prune it? </li>
<li>Is it a good idea to combine multiple trees? </li>
<li>What is Random Forest? Why is it good?</li>
<li>Other ways to combine trees? What about boosting?</li>
</ul>
<p>Logistic regression:</p>
<ul>
<li>What is logistic regression? </li>
<li>How do we train a logistic regression model?</li>
<li>How do we interpret its coefficients?</li>
</ul>
<p>Support Vector Machines</p>
<ul>
<li>What is the maximal margin classifier? How this margin can be achieved and why is it beneficial?</li>
<li>How do we train SVM? What about hard SVM and soft SVM?</li>
<li>What is a kernel? What's the intuition behind the Kernel trick?</li>
<li>Which kernels do you know? How to choose a kernel? </li>
</ul>
<p>Neural Networks</p>
<ul>
<li>What is an Artificial Neural Network?</li>
<li>How to train an ANN? What is back propagation? </li>
<li>How does a neural network with three layers (one input layer, one inner layer and one output layer) compare to a logistic regression?</li>
<li>What is deep learning? What is CNN (Convolution Neural Network) or RNN (Recurrent Neural Network)? </li>
</ul>
<p>Other models: </p>
<ul>
<li>What other models do you know? </li>
<li>How can we use Naive Bayes classifier for categorical features? What if some features are numerical? </li>
<li>Tradeoffs between different types of classification models. How to choose the best one? </li>
<li>Compare logistic regression with decision trees and neural networks.</li>
</ul>
<p> </p>
<h3 id="regularization">Regularization</h3>
<ul>
<li>What is Regularization? </li>
<li>Which problem does Regularization try to solve? </li>
<li>What does it mean (practically) for a design matrix to be “ill-conditioned”?</li>
<li>When might you want to use ridge regression instead of traditional linear regression?</li>
<li>What is the difference between the <script id="MathJax-Element-27" type="math/tex">L_1</script> and <script id="MathJax-Element-28" type="math/tex">L_2</script> regularization?</li>
<li>Why (geometrically) does LASSO produce solutions with zero-valued coefficients (as opposed to ridge)?</li>
<li>Let us go through the derivation of OLS or Logistic Regression. What happens when we add <script id="MathJax-Element-29" type="math/tex">L_2</script> regularization? How do the derivations change? What if we replace <script id="MathJax-Element-30" type="math/tex">L_2</script> regularization with <script id="MathJax-Element-31" type="math/tex">L_1</script> regularization?</li>
</ul>
<p> </p>
<h3 id="dimensionality-reduction">Dimensionality Reduction</h3>
<p>Basics:</p>
<ul>
<li>What is the purpose of dimensionality reduction and why do we need it? </li>
<li>Are dimensionality reduction techniques supervised or not? Are all of them are (un)supervised? </li>
<li>What ways of reducing dimensionality do you know? </li>
<li>Is feature selection a dimensionality reduction technique? </li>
<li>What is the difference between feature selection and feature extraction? </li>
<li>Is it beneficial to perform dimensionality reduction before fitting an SVM? Why or why not?</li>
</ul>
<p>Principal Component Analysis:</p>
<ul>
<li>What is Principal Component Analysis (PCA)? What is the problem it solves? How is it related to eigenvalue decomposition (EVD)? </li>
<li>What’s the relationship between PCA and SVD? When SVD is better than EVD for PCA?</li>
<li>Under what conditions is PCA effective?</li>
<li>Why do we need to center data for PCA and what can happed if we don’t do it? Do we need to scale data for PCA? </li>
<li>Is PCA a linear model or not? Why? </li>
</ul>
<p>Other Dimensionality Reduction techniques:</p>
<ul>
<li>Do you know other Dimensionality Reduction techniques? </li>
<li>What is Independent Component Analysis (ICA)? What’s the difference between ICA and PCA? </li>
<li>Suppose you have a very sparse matrix where rows are highly dimensional. You project these rows on a random vector of relatively small dimensionality. Is it a valid dimensionality reduction technique or not? </li>
<li>Have you heard of Kernel PCA or other non-linear dimensionality reduction techniques? What about LLE (Locally Linear Embedding) or <script id="MathJax-Element-32" type="math/tex">t</script>-SNE (<script id="MathJax-Element-33" type="math/tex">t</script>-distributed Stochastic Neighbor Embedding)</li>
<li>What is Fisher Discriminant Analysis? How it is different from PCA? Is it supervised or not? </li>
</ul>
<p> </p>
<h3 id="cluster-analysis">Cluster Analysis</h3>
<ul>
<li>What is the cluster analysis problem?</li>
<li>Which cluster analysis methods you know? </li>
<li>Describe <script id="MathJax-Element-34" type="math/tex">K</script>-Means. What is the objective of <script id="MathJax-Element-35" type="math/tex">K</script>-Means? Can you describe the Lloyd algorithm? </li>
<li>How do you select <script id="MathJax-Element-36" type="math/tex">K</script> for K-Means? </li>
<li>How can you modify <script id="MathJax-Element-37" type="math/tex">K</script>-Means to produce soft class assignments?</li>
<li>How to assess the quality of clustering? </li>
<li>Describe any other cluster analysis method. E.g. DBSCAN.</li>
</ul>
<p> </p>
<h3 id="optimization">Optimization</h3>
<p>You may have some basic questions about optimization:</p>
<ul>
<li>What is the difference between a convex function and non-convex? </li>
<li>What is Gradient Descent Method?</li>
<li>Will Gradient Descent methods always converge to the same point?</li>
<li>What is a local optimum? </li>
<li>Is it always bad to have local optima?</li>
</ul>
<p> </p>
<h3 id="recommendation">Recommendation</h3>
<ul>
<li>What is a recommendation engine? How does it work?</li>
<li>Do you know about the Netflix Prize problem? How would you approach it?</li>
<li>How to do customer recommendation?</li>
<li>What is Collaborative Filtering?</li>
<li>How would you generate related searches for a search engine?</li>
<li>How would you suggest followers on Twitter?</li>
</ul>
<p> </p>
<h3 id="feature-engineering">Feature Engineering</h3>
<ul>
<li>How to apply Machine Learning to audio data, images, texts, graphs, etc? </li>
<li>What is Feature Engineering? Can you give an example? Why do we need it? </li>
<li>How to go from categorical variables to numerical? </li>
<li>What to do with categorical variables of high cardinality?</li>
</ul>
<p> </p>
<h3 id="natural-language-processing">Natural Language Processing</h3>
<p>If the company deals with text data, you can expect some questions on NLP and Information Retrieval:</p>
<ul>
<li>What is NLP? How is it related to Machine Learning? </li>
<li>How would you turn unstructured text data into structured data usable for ML models?</li>
<li>What is the Vector Space Model?</li>
<li>What is TF-IDF?</li>
<li>Which distances and similarity measures can we use to compare documents? What is cosine similarity? </li>
<li>Why do we remove stop words? When do we not remove them?</li>
<li>Language Models. What is <script id="MathJax-Element-38" type="math/tex">N</script>-Grams? </li>
<li>What is word2vec? How it can be used in NLP and IR?</li>
</ul>
<p> </p>
<h3 id="meta-learning">Meta Learning</h3>
<p>Feature Selection:</p>
<ul>
<li>Are all features equally good? </li>
<li>What are the downfalls of using too many or too few variables?</li>
<li>How many features should you use? How do you select the best features? </li>
<li>What is Feature Selection and why do we need it? </li>
<li>Describe several feature selection methods. Are these methods depend on the model or not?</li>
</ul>
<p>Model selection:</p>
<ul>
<li>You have built several different models. How would you select the best one? </li>
<li>You have one model and want to find the best set of parameters for this model. How would you do that? </li>
<li>How would you look for the best parameters? Do you know something else apart from grid search? </li>
<li>What is Cross-Validation? </li>
<li>What is 10-Fold CV?</li>
<li>What is the difference between holding out a validation set and doing 10-Fold CV?</li>
</ul>
<p>Model evaluation</p>
<ul>
<li>How do you know if your model overfits? </li>
<li>How do you assess the results of a logistic regression?</li>
<li>Which evaluation metrics you know? Something apart from accuracy?</li>
<li>Which is better: Too many false positives or too many false negatives?</li>
<li>What precision and recall are?</li>
<li>What is a ROC curve? What is AU ROC (AUC)? How to interpret the curve and AU ROC? </li>
<li>Do you know about Concordance or Lift? </li>
</ul>
<p>Discussion Questions:</p>
<ul>
<li>You have a marketing campaign and you want to send emails to users. You developed a model for predicting if a user will reply or not. How can you evaluate this model? Is there a chart you can use?</li>
</ul>
<p> </p>
<h3 id="miscellanea">Miscellanea</h3>
<p>Curse of Dimensionality</p>
<ul>
<li>What is Curse of Dimensionality? How does it affect distance and similarity measures? </li>
<li>What are the problems of large feature space? How does it affect different models, e.g. OLS? What about computational complexity? </li>
<li>What dimensionality reductions can be used for preprocessing the data?</li>
<li>What is the difference between density-sparse data and dimensionally-sparse data? </li>
</ul>
<p>Others </p>
<ul>
<li>You are training an image classifier with limited data. What are some ways you can augment your dataset?</li>
</ul>
<p> </p>
<h2 id="computer-science">Computer Science</h2>
<p>Knowledge in Computer Science is as important for Data Science as knowledge in Machine Learning. So you may get the same type of questions as for any software developer position, but possibly with lower expectations on your answers.</p>
<p>I was a Java developer for quite some time, and I prepared a list of questions I asked (and often was asked) on Java interviews: <a href="http://www.itshared.org/2015/09/java-interview-questions.html">Java Inteview questions</a>. This list can also be helpful for preparing to a Data Science interview.</p>
<p> </p>
<h3 id="libraries-and-tools">Libraries and Tools</h3>
<p>Apart from basics of Java/Scala/Python/etc, you may be asked about libraries for data analysis:</p>
<ul>
<li>Which libraries for data analysis do you know in Python/R/Java?</li>
<li>Have you used numpy, scipy, pandas, sklearn?</li>
<li>What are some features of the sklearn api that differentiate it from fitting models in R?</li>
<li>What are some features of pandas/sklearn that you like? Don't like? Same questions for R.</li>
<li>Why is “vectorization” such a powerful method for optimizing numerical code? What is going on that makes the code faster relative to alternatives like nested for loops?</li>
<li>When is it better to write your own code than using a data science software package?</li>
<li>State any 3 positive and negative aspects about your favorite statistical software.</li>
<li>Describe a difficult bug you’ve encountered and how you resolved it.</li>
<li>How does floating point affect precision of calculations? Equality tests?</li>
<li>What is BLAS? LAPACK?</li>
</ul>
<p> </p>
<h3 id="databases">Databases</h3>
<ul>
<li>Have you been involved in database design and data modeling?</li>
<li>SQL-Related questions: e.g. what is "group by"? </li>
<li>Or given some DB schema you may be asked to write a simple SQL query.</li>
<li>What is a “star schema”? “snowflake schema”?</li>
<li>Describe different NoSQL technologies you’re familiar with, what they are good at, and what they are bad at.</li>
</ul>
<p> </p>
<h3 id="distributed-systems-and-big-data">Distributed Systems and Big Data</h3>
<p>Basic “Big Data” questions:</p>
<ul>
<li>What is the biggest data set that you have processed and how did you process it? What was the result?</li>
<li>Have you used Apache Hadoop, Apache Spark, Apache Flink? Why? Have you used Apache Mahout? </li>
</ul>
<p>MapReduce</p>
<ul>
<li>What is MapReduce? Why is it “shared-nothing” architecture?</li>
<li>Can you implement word count in MapReduce? What about something a bit more complex like TF-IDF? Naive Bayes? </li>
<li>What is load balance? How to make sure a MapReduce application has good load balance? </li>
<li>Can you give examples where MapReduce does not work? </li>
<li>What are examples of “embarassingly parallelizable” algorithms?</li>
<li>How would you estimate the median of a dataset that is too big to hold in the memory?</li>
</ul>
<p>Implementation questions</p>
<p> </p>
<p>There are some posts that you may find useful when preparing for the “Big Data” part: </p>
<ul>
<li><a href="http://www.itshared.org/2015/03/hadoop-and-mapreduce.html">Hadoop and MapReduce</a></li>
<li><a href="http://www.itshared.org/2015/03/naive-bayes-on-apache-flink.html">Naive Bayes on Apache Flink</a></li>
</ul>
<p> </p>
<h2 id="hands-on">Hands-On</h2>
<p>Also, many interviews have a part which I call “hands-on”: you are given some problem description and you are asked to solve it. You can just talk the interviewers through your solution or even be asked to sit and implement some parts. Sometimes there is also a test assignment to be done at home (prior to the interview). </p>
<p> </p>
<h3 id="problem-to-solve">Problem to Solve</h3>
<p>For example:</p>
<p>Assume that you are asked to lead a project on churn detection, and have dataset of known users who stopped using the service and ones who are still using. This data includes demographics and other features.</p>
<p>Do the following:</p>
<ol>
<li>Describe the methodology and model that you will chose to identify churn, and describe your thought process.</li>
<li>Think how would you communicate the results to the CEO? </li>
<li>Suppose in the dataset only 0.025 of users churned. How would you make it more balanced? </li>
</ol>
<p>Also:</p>
<ul>
<li>How would you implement it if you had one day? One month? One year? </li>
<li>How would your approach scale? </li>
</ul>
<p>Other problems: </p>
<ul>
<li>How would you approach identifying plagiarism? </li>
<li>How to find individual paid accounts shared by multiple users?</li>
<li>How to detect bogus reviews, or bogus Facebook accounts used for bad purposes?</li>
<li>Usually the domain of the problem is related to what the company is doing. If they’re doing marketing, it will most likely be marketing related. </li>
</ul>
<p>Additionally, you may be asked:</p>
<ul>
<li>How would you approach collecting the data if you didn’t have the dataset? </li>
</ul>
<p> </p>
<h3 id="coding">Coding</h3>
<p>Sometimes you even may be presented a small dataset and ask to do a particular task with any tool. For example, </p>
<ul>
<li>write a script to extract features, </li>
<li>then do some exploratory data analysis and </li>
<li>finally apply some ML algorithm to this dataset. </li>
</ul>
<p>Or just the last two, with a ready to use dataset in tabular form. </p>
<p> </p>
<h2 id="sources">Sources</h2>
<p>I had to work through a lot of sources to make this compilation. I did not include all the questions I came across, just the ones that made sense or ones I really got during my interviews. It also, of course, includes my own interviews. </p>
<p>Here is the list of sources I used: </p>
<ul>
<li><a href="http://www.quora.com/What-is-a-typical-data-scientist-interview-like">http://www.quora.com/What-is-a-typical-data-scientist-interview-like</a></li>
<li><a href="http://www.quora.com/What-are-the-interview-questions-on-regression-modeling">http://www.quora.com/What-are-the-interview-questions-on-regression-modeling</a></li>
<li><a href="http://www.quora.com/How-should-I-prepare-for-statistics-questions-for-a-data-science-interview">http://www.quora.com/How-should-I-prepare-for-statistics-questions-for-a-data-science-interview</a></li>
<li><a href="http://www.quora.com/A-B-Testing/What-kind-of-A-B-testing-questions-should-I-expect-in-a-data-scientist-interview-and-how-should-I-prepare-for-such-questions">http://www.quora.com/A-B-Testing/What-kind-of-A-B-testing-questions-should-I-expect-in-a-data-scientist-interview-and-how-should-I-prepare-for-such-questions</a></li>
<li><a href="http://www.quora.com/What-are-20-questions-to-detect-fake-data-scientists">http://www.quora.com/What-are-20-questions-to-detect-fake-data-scientists</a></li>
<li><a href="http://www.quora.com/What-are-some-common-Machine-Learning-interview-questions">http://www.quora.com/What-are-some-common-Machine-Learning-interview-questions</a></li>
<li><a href="http://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher">http://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher</a></li>
<li><a href="https://www.quora.com/Are-CS-questions-part-of-a-data-scientist-interview-at-Facebook">https://www.quora.com/Are-CS-questions-part-of-a-data-scientist-interview-at-Facebook</a></li>
<li><a href="http://www.quora.com/Data-Science/How-should-I-prepare-for-statistics-questions-for-a-data-science-interview">http://www.quora.com/Data-Science/How-should-I-prepare-for-statistics-questions-for-a-data-science-interview</a></li>
<li><a href="http://stats.stackexchange.com/questions/5465/statistics-interview-questions">http://stats.stackexchange.com/questions/5465/statistics-interview-questions</a></li>
<li><a href="http://www.reddit.com/r/datascience/comments/2nhb4k/what_interview_questions_have_you_been_asked/">http://www.reddit.com/r/datascience/comments/2nhb4k/what_interview_questions_have_you_been_asked/</a></li>
<li><a href="http://www.reddit.com/r/statistics/comments/310h76/i_have_an_interview_for_a_parttime_data_analyst/">http://www.reddit.com/r/statistics/comments/310h76/i_have_an_interview_for_a_parttime_data_analyst/</a></li>
<li><a href="https://www.reddit.com/r/datascience/comments/3fsz54/my_top10_technical_questions_for_job_candidates/">https://www.reddit.com/r/datascience/comments/3fsz54/my_top10_technical_questions_for_job_candidates/</a></li>
<li><a href="https://www.reddit.com/r/datascience/comments/3kzf69/data_scientist_interview_questions_on_pca_svm/">https://www.reddit.com/r/datascience/comments/3kzf69/data_scientist_interview_questions_on_pca_svm/</a></li>
<li><a href="http://www.reddit.com/r/MachineLearning/comments/392nwy/interview_questions_for_data_scientist_positions/">http://www.reddit.com/r/MachineLearning/comments/392nwy/interview_questions_for_data_scientist_positions/</a></li>
<li><a href="http://blog.udacity.com/2015/04/data-science-interview-questions.html">http://blog.udacity.com/2015/04/data-science-interview-questions.html</a></li>
<li><a href="http://alyaabbott.wordpress.com/2014/10/01/how-to-ace-a-data-science-interview/">http://alyaabbott.wordpress.com/2014/10/01/how-to-ace-a-data-science-interview/</a></li>
<li><a href="http://www.marketingdistillery.com/2014/09/03/how-to-successfully-recruit-a-data-scientist/">http://www.marketingdistillery.com/2014/09/03/how-to-successfully-recruit-a-data-scientist/</a> </li>
<li><a href="http://www.edureka.co/blog/frequently-asked-data-science-interview-questions">http://www.edureka.co/blog/frequently-asked-data-science-interview-questions</a></li>
<li><a href="http://www.galvanize.it/blog/how-to-nail-a-data-science-interview">http://www.galvanize.it/blog/how-to-nail-a-data-science-interview</a></li>
<li><a href="http://analyticsindiamag.com/common-analytics-interview-questions/">http://analyticsindiamag.com/common-analytics-interview-questions/</a></li>
<li><a href="http://www.datasciencecentral.com/profiles/blogs/66-job-interview-questions-for-data-scientists">http://www.datasciencecentral.com/profiles/blogs/66-job-interview-questions-for-data-scientists</a></li>
</ul>
<p> </p>
<h2 id="useful-links">Useful Links</h2>
<p>If you are preparing to a Data Science interview, you may also find the following links useful:</p>
<ul>
<li><a href="http://www2.udacity.com/rs/udacity/images/Ultimate%20Skills%20Checklist%20For%20Your%20First%20Data%20Analyst%20Job.pdf">http://www2.udacity.com/rs/udacity/images/Ultimate%20Skills%20Checklist%20For%20Your%20First%20Data%20Analyst%20Job.pdf</a></li>
<li><a href="http://www.quora.com/What-are-some-important-questions-to-ask-a-recruiter-when-interviewing-for-a-data-science-job">http://www.quora.com/What-are-some-important-questions-to-ask-a-recruiter-when-interviewing-for-a-data-science-job</a></li>
<li><a href="http://www.quora.com/In-a-data-scientist-interview-should-I-use-Python-or-C++-for-algorithm-data-structure-questions">http://www.quora.com/In-a-data-scientist-interview-should-I-use-Python-or-C++-for-algorithm-data-structure-questions</a></li>
<li><a href="http://www.quora.com/How-do-I-prepare-for-a-data-scientist-interview">http://www.quora.com/How-do-I-prepare-for-a-data-scientist-interview</a></li>
<li><a href="http://datascienceinterview.quora.com/Data-Science-Interview-Preparation">http://datascienceinterview.quora.com/Data-Science-Interview-Preparation</a></li>
<li><a href="http://datascienceinterview.quora.com/Answers-1">http://datascienceinterview.quora.com/Answers-1</a></li>
<li><a href="https://github.com/gkamradt/Lessons-Learned-Data-Science-Interviews">https://github.com/gkamradt/Lessons-Learned-Data-Science-Interviews</a></li>
<li><a href="http://mathewanalytics.com/2015/08/18/homework-during-the-hiring-process-no-thanks/">http://mathewanalytics.com/2015/08/18/homework-during-the-hiring-process-no-thanks/</a></li>
<li><a href="https://medium.com/@D33B/interview-questions-for-data-scientist-positions-5ad3c5d5b8bd">https://medium.com/@D33B/interview-questions-for-data-scientist-positions-5ad3c5d5b8bd</a></li>
<li><a href="https://medium.com/@D33B/interview-questions-for-data-scientist-positions-part-ii-ac294c2c7241">https://medium.com/@D33B/interview-questions-for-data-scientist-positions-part-ii-ac294c2c7241</a></li>
<li><a href="http://www.jasq.org/just-another-scala-quant/new-agey-interviews-at-the-grocery-startup">http://www.jasq.org/just-another-scala-quant/new-agey-interviews-at-the-grocery-startup</a></li>
<li><a href="http://www.erinshellman.com/crushed-it-landing-a-data-science-job/">http://www.erinshellman.com/crushed-it-landing-a-data-science-job/</a></li>
<li><a href="http://treycausey.com/data_science_interviews.html">http://treycausey.com/data_science_interviews.html</a></li>
<li><a href="http://nadbordrozd.github.io/interviews/">http://nadbordrozd.github.io/interviews/</a></li>
<li><a href="http://rpubs.com/JDAHAN/172473">http://rpubs.com/JDAHAN/172473</a></li>
</ul>
<p> </p>
<h2 id="the-end">The End</h2>
<p>Even though the post was lengthy, I hope you enjoyed it and found this information useful. Happy interviewing! And please do let us know if you got any interesting questions that we should add.</p>
<p><b>Update</b>: I'm very pleased that this post is getting popular, but some people started copying the questions from here to their blog posts without proper attribution. If you copy some questions from here, I would be very grateful if you mentioned the source.</p>
<p><right><i>Last updated: 09.06.2016</i></right></p>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com4tag:blogger.com,1999:blog-7332477090953898587.post-82181306881852991122015-10-18T12:00:00.000+02:002015-10-21T14:15:12.748+02:00Java Interview Questions<h1 id="java-interview-questions">Java Interview Questions</h1>
<p><center><img src="https://habrastorage.org/files/acd/dc7/52a/acddc752a3a8473fb4532cf6da8be6e3.jpg"/></center></p>
<p>In the past, I was a Software Developer, and my primary programming language was Java. I also quite often interviewed people and also sometimes was an interviewee. In this post, I would like to share typical questions that you might expect at a job interview for a Java Developer position. </p>
<a name='more'></a>
<p>Let’s get started!</p>
<h2 id="table-of-content">Table of Content</h2>
<ul>
<li><a href="#warm-up">Warm-up</a></li>
<li><a href="#algorithms-data-structures">Algorithms & Data structures</a></li>
<li><a href="#java-core">Java Core</a><ul>
<li><a href="#basics">Basics</a></li>
<li><a href="#exceptions">Exceptions</a></li>
<li><a href="#collections">Collections</a></li>
<li><a href="#generics">Generics</a></li>
<li><a href="#multithreading">Multithreading</a></li>
<li><a href="#java-io-and-nio">Java IO and NIO</a></li>
<li><a href="#memory-and-garbage-collector">Memory and Garbage Collector</a></li>
<li><a href="#unit-testing">Unit testing</a></li></ul>
</li>
<li><a href="#database-questions">Database Questions</a><ul>
<li><a href="#database-engines">Database Engines</a></li>
<li><a href="#sql">SQL</a></li></ul></li>
<li><a href="#xml">XML</a></li>
<li><a href="#libraries">Libraries</a>
<ul><li><a href="#spring">Spring</a></li></ul></li>
<li><a href="#software-design">Software Design</a>
<ul><li><a href="#design-patterns">Design patterns</a></li>
<li><a href="#oop">OOP</a></li></ul>
</li>
</ul>
<p> </p>
<h2 id="warm-up">Warm-up</h2>
<ul>
<li>What programming languages do you know and use?</li>
<li>What is your most hated technology or tool and why?</li>
<li>Which technology do you consider yourself expert in?</li>
<li>What was the biggest challenge you encountered? How did you deal with it?</li>
<li>Tell me about your biggest professional achievement</li>
<li>What was the last book you’ve read? What was the most important point you remembered from the book?</li>
</ul>
<p> </p>
<h2 id="algorithms-data-structures">Algorithms & Data structures</h2>
<ul>
<li>Which data structures do you know? Tell me about one of them</li>
<li>What is recursion? tail recursion, mutual recursion?</li>
<li>Complexity of algorithms, big <script id="MathJax-Element-1" type="math/tex">O</script> notation</li>
<li>Which sorting algorithms do you know?</li>
<li>How to sort a linked list?</li>
<li>Linear-time sorting (count sort)?</li>
<li>Trees, graphs and ways to traverse graph</li>
<li>What is a binary tree?</li>
<li>How to search in binary tree?</li>
<li>Self balanced trees (Red-black tree, AVL, Splay)</li>
<li>Tries, prefix and suffix trees</li>
<li>What is NP-completeness? What algorithms are NP-Complete?</li>
<li>What is the divide and conquer approach?</li>
<li>Tell me about Dynamic programming </li>
<li>What is MapReduce?</li>
</ul>
<p> </p>
<h2 id="java-core">Java Core</h2>
<h3 id="basics">Basics</h3>
<ul>
<li>What access modifiers do you know in Java? </li>
<li>What is the contract between <code>equals()</code> and <code>hashCode()</code>?</li>
<li>Rules to implement the <code>equals</code> method (stability, transitivity, reflectivity etc)?</li>
<li>Why do we need nested classes? </li>
<li>What is immutability? Why is it good? </li>
<li>How to make a class immutable?</li>
<li>Besides <code>String</code> do you know any other immutable classes from JDK?</li>
<li>Can a interface inherit from another interface?</li>
<li>What is the difference between regular vs. static initialization blocks?</li>
</ul>
<p> </p>
<h3 id="exceptions">Exceptions</h3>
<ul>
<li>How exceptions are handled in Java?</li>
<li>Java Exceptions API. What standard exceptions do you know? </li>
<li>Checked vs unchecked exceptions. When would you use them?</li>
<li>Is there any difference between handing <code>Error</code> and <code>Exception</code>?</li>
<li>Can we have only <code>try</code> and <code>finally</code> without <code>catch</code>? If yes, when is it useful? </li>
<li>What is try-with-resource? </li>
<li>Is it possible that the <code>finally</code> block is not executed? When? </li>
</ul>
<p> </p>
<h3 id="collections">Collections</h3>
<ul>
<li>Collection API. Can you list general collection API? (e.g. <code>Collection</code>, <code>Set</code>, <code>Map</code>, <code>List</code>, <code>Queue</code>, <code>SortedSet</code>, <code>SortedMap</code>, etc)</li>
<li>Can you say how these interfaces are related? </li>
<li>Does <code>Map</code> extend the <code>Collection</code> interface?</li>
<li>What is the difference between <code>ArrayList</code> and <code>LinkedList</code>?</li>
<li>What is the difference between <code>Stack</code> and <code>Queue</code>?</li>
<li><code>TreeSet</code> vs <code>LinkedHashSet</code></li>
<li>How <code>HashMap</code> is implemented? How collisions are resolved there?</li>
<li>How to implement <code>hashCode</code> to achieve the best performance?</li>
<li>What is the difference between <code>Hashtable</code> and <code>ConcurrentHashMap</code>?</li>
<li>Do you know how <code>ConcurrentHashMap</code> is implemented? How is it different from usual <code>HashMap</code> or <code>Hashtable</code>?</li>
<li>Special implementations. Why do we need <code>EnumSet</code>, <code>EnumMap</code>? What about <code>WeakHaskMap</code> or <code>IdentityHashMap</code>?</li>
<li>What is <code>ConcurentModificationException</code>? How can we get it? How to avoid getting it?</li>
<li>Collections with safe iterators (<code>CopyOnWriteArrayList</code>/<code>CopyOnWriteArraySet</code>)</li>
</ul>
<p> </p>
<h3 id="generics">Generics</h3>
<ul>
<li>What is a parameterized or generic type?</li>
<li>Can we use parameterized types in exception handling?</li>
<li>What is a wildcard parameterized type?</li>
<li>What is autoboxing and what are its advantages/pitfalls?</li>
<li>Can we add something to <code>List<?></code>?</li>
</ul>
<p> </p>
<h3 id="multithreading">Multithreading</h3>
<ul>
<li>How to make multithreaded code? </li>
<li>What’s the difference between extending <code>Thread</code> vs implementing <code>Runnable</code>? </li>
<li>What is the difference between <code>Thread</code>’s methods <code>run()</code> and <code>start()</code>?</li>
<li>Synchronization of java blocks and methods</li>
<li>How to use <code>wait</code> and <code>notify</code>?</li>
<li>What is the difference between <code>Thread.sleep</code> and <code>wait</code>?</li>
<li>What is an atomic operation? Is <code>i++</code> atomic? </li>
<li>What does the <code>volatile</code> keyword mean?</li>
<li>Have you used <code>java.util.concurrent.*</code>? What have you used from there?</li>
<li>Why do we need <code>ThreadLocal</code>?</li>
<li>What is a deadlock? Can you give an example? How to cause it?</li>
<li>What is a livelock? </li>
<li>What is starvation? </li>
<li>What is the race condition?</li>
<li>Atomicity of long and double assignment operations</li>
<li>Lock-free operations. How to create lock-free implementation of field reassignment?</li>
<li>How to interrupt a thread?</li>
</ul>
<p> </p>
<h3 id="java-io-and-nio">Java IO and NIO</h3>
<ul>
<li>Can you describe the standard Java IO API?</li>
<li>What is the difference between <code>InputStream</code> and <code>Reader</code>? <code>OutputStream</code> and <code>Writer</code>?</li>
<li>How does <code>BufferedReader</code> work?</li>
<li>What is NIO?</li>
<li>What is <code>Channel</code>? What is <code>Buffer</code>?</li>
<li>How to lock a file?</li>
<li>What is NIO2?</li>
</ul>
<p> </p>
<h3 id="memory-and-garbage-collector">Memory and Garbage Collector</h3>
<ul>
<li>Memory model in JVM</li>
<li>How does virtual space divided in Java? What is permgen? Is it still there in Java 8? </li>
<li>What difference between float and BigDecimal? How they store the data?</li>
<li>What is deep copy of a Java object?</li>
<li>What are the disadvantages of setting heap size too high?</li>
<li>What are utilities for JVM monitoring? What is Jconsole?</li>
<li>How to force GC be executed?</li>
<li>Garbage collection principles</li>
<li>What is a memory leak? How and why it can be caused? </li>
<li>What is variable shadowing?</li>
<li>How would you monitor JVM?</li>
<li>How would you monitor how GC behaves during program execution?</li>
<li>Name few GC implementations (Serial, Parallel, ParallelOld, ConcarentMarkAndSweep, G1) and describe major differences</li>
</ul>
<p> </p>
<h3 id="unit-testing">Unit testing</h3>
<ul>
<li>What is unit testing? </li>
<li>Libraries that help to writing unit tests</li>
<li>Why do we need JUnit?</li>
<li>What is a mock? Have you used EasyMock or Mockito?</li>
</ul>
<p> </p>
<h2 id="database-questions">Database Questions</h2>
<h3 id="database-engines">Database Engines</h3>
<ul>
<li>Does every DBMS system implement SQL in the same way? </li>
<li>What’s the difference between a table and a view? </li>
<li>What is a transaction? What is ACID? </li>
<li>Which transaction isolation levels do you know?</li>
<li>What is the difference between primary and secondary keys? </li>
<li>What types of constraints do you know?</li>
<li>What is a materialized view? How is it different from usual view? </li>
<li>What kind of joins do you know?</li>
<li>What is the difference between inner join and outer join?</li>
<li>Why do we need indexes? </li>
<li>Is it a good idea to have many different indexes? Why or why not? </li>
<li>What is a bitmap index?</li>
<li>How <code>SELECT</code>, <code>UPDATE</code> and <code>DELETE</code> statements are affected by indexes? </li>
<li>What is de-normalization and why would we do it? </li>
<li>How can we improve database performance? </li>
<li>Partitioning and methods of partitioning </li>
</ul>
<p> </p>
<h3 id="sql">SQL</h3>
<ul>
<li>Aggregate functions with examples</li>
<li>Given a schema, write some SQL query with group by / outer join / etc. </li>
<li>What is a nested subquery?</li>
</ul>
<p> </p>
<h2 id="xml">XML</h2>
<ul>
<li>Did you need to process XML? How and why?</li>
<li>What is XPath? Have you used it? </li>
<li>Experience with XSLT</li>
<li>SAX vs DOM</li>
<li>DTD vs XMLSchema</li>
<li>What is the difference between HTML and XML?</li>
<li>What is well formed HTML?</li>
<li>What is XML namespace? Why is it useful?</li>
<li>What characters are disallowed from XML without escaping?</li>
</ul>
<p> </p>
<h2 id="libraries">Libraries</h2>
<p>You are very likely to have questions about Spring, Hibernate, or any other frameworks used by the company. </p>
<h3 id="spring">Spring</h3>
<ul>
<li>What is Inversion of Control? Benefits?</li>
<li>What is Spring configuration file? How does it look like?</li>
<li>Out of the box bean scopes (singleton, prototype; request, session, global session)</li>
<li>Autowiring. Types of autowiring.</li>
<li>What modules does Spring Framework have?</li>
<li>Describe AOP integration in Spring</li>
</ul>
<p> </p>
<h2 id="software-design">Software Design</h2>
<h3 id="design-patterns">Design patterns</h3>
<ul>
<li>What design patterns are in JDK? Can you give some examples? What about Java IO API?</li>
<li>Which patterns do you use on a daily basis? Explain their principles.</li>
<li>What is Façade/Proxy/Decorator/Strategy/Observer/etc? (selectively)</li>
</ul>
<p> </p>
<h3 id="oop">OOP</h3>
<ul>
<li>Inheritance vs Composition. Which is better? Why?</li>
<li>What is Coupling and Cohesion?</li>
<li>What is SOLID? Do you know what each letter stands for? </li>
<li>What is Single Responsibility Principle? Interface segregation principle? Dependency inversion principle?</li>
<li>Can you tell me about the DRY principle? </li>
<li>What is the Law of Demeter?</li>
<li>What is Feature Envy?</li>
<li>Do you know about CQRS (Command Query Responsibility Segregation)?</li>
<li>Principle of Least Astonishment/Surprise</li>
</ul>
<p> </p>
<h2 id="the-end">The end</h2>
<p>The main source of the questions is job interviews we conducted at <a href="http://www.luxoft.com/">Luxoft</a> a few years ago while I was working as a Java Developer. This of course is by no means a full list, but I hope it can still be useful for your interview preparation. </p>
<p>If you’re also interested in Data Science job interview questions, then go <a href="http://www.itshared.org/2015/10/data-science-interview-questions.html">here</a>.</p>
<p>And finally, stay tuned for our new blog posts! </p>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-6105052628094206332015-10-15T11:25:00.001+02:002015-10-15T16:46:06.603+02:00Mastering Data Analysis with R<div dir="ltr" style="text-align: left;" trbidi="on">
<br />
<center>
<img border="0" src="https://hsto.org/files/0ed/93c/a79/0ed93ca794a44e46bb88dd6f9157d04b.jpg" style="max-height: 100%; max-width: 100%;" /></center>
<br />
The book "Mastering Data Analysis with R" by Gergely Daróczi, published by Packt Publishing, is in print now, and I had a pleasure to be a technical reviewer of this book.<br />
<br />
If you're a Data Scientist who's looking to master R, this book is a good choice. It's already available on the <a href="https://www.packtpub.com/big-data-and-business-intelligence/mastering-data-analysis-r">publisher's website</a> and on
<a href="https://www.safaribooksonline.com/library/view/mastering-data-analysis/9781783982028/">Safari books online</a>.<br /><br />
<a name='more'></a><br />
<h3>
Table of Content</h3>
<ol>
<li>Hello, Data!</li>
<li>Getting Data from the Web</li>
<li>Filtering and Summarizing Data</li>
<li>Restructuring Data</li>
<li>Building Models</li>
<li>Beyond the Linear Trend Line</li>
<li>Unstructured Data</li>
<li>Polishing Data</li>
<li>From Big to Small Data</li>
<li>Classification and Clustering</li>
<li>Social Network Analysis of the R Ecosystem</li>
<li>Analyzing Time-series</li>
<li>Data Around Us</li>
<li>Analyzing the R Community</li>
</ol>
<br/>
<h3>
What You Will Learn </h3>
<ul>
<li>Connect to and load data from R's range of powerful databases</li>
<li>Successfully fetch and parse structured and unstructured data</li>
<li>Transform and restructure your data with efficient R packages</li>
<li>Define and build complex statistical models with glm</li>
<li>Develop and train machine learning algorithms</li>
<li>Visualize social networks and graph data</li>
<li>Deploy supervised and unsupervised classification algorithms</li>
<li>Discover how to visualize spatial data with R</li>
</ul>
<ol>
</ol>
</div>
Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com3tag:blogger.com,1999:blog-7332477090953898587.post-41149090531900975962015-06-11T00:21:00.000+02:002015-06-11T00:23:05.728+02:00Recognition Award for IT4BI<p>The French <i>Institute for Research in Computer Science and Automation (INRIA)</i> has awarded an ongoing <b><i><a href="http://it4bi.univ-tours.fr">IT4BI</i> Master </a>Thesis Project</b> with the prize <b><i>"Prix spécial du jury"</i></b> in the context of the competition <i>"<a href="http://www.inria.fr/actualite/actualites-inria/palmares-de-boost-your-code-2015">Boost Your Code 2015</a>"</i>.</p>
<p>The award was given for <b><i>ElectioVis</i></b>, an open source <b>decision-aiding</b> software tool that I started to develop as part of the <i>IT4BI <b><a href="http://www.ecp.fr/M2-Information-Technologies-for-Business-Intelligence">Decision Support and BI</a></i></b> specialisation I am pursuing at <b><i>École Centrale Paris</i></b>. The project profits from the academic advisory of <a href="https://it.linkedin.com/pub/valentina-ferretti/11/46b/a10">Prof. Valentina Ferretti</a>, an expert in the decision-making field who lectures at <i>École Centrale Paris</i> and <i>Polytechnic University of Turin</i>.</p>
<p><i>ElectioVis</i> is a website that aims to bring the <b>power of decision-making</b> closer to <b>all citizens of the world</b>, overcoming economic, social, cognitive and language barriers. It will be <b>available online during June 2015</b> and everyone will be able to <b>try it for free</b>.</p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfxfi7RSccQxlMzwcPa10oqBQeCbxGDYBCC1uCiQuDAW55lB9aWa5zNU0mzim-uqfkeFFTJ6XzBRkFdv9U3mpTPNQyoav7LWvl-fxBljiBaCkrjHVOWucBqKFXImMtZEulQg-7MBowlIjE/s1600/electiovis_english.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjfxfi7RSccQxlMzwcPa10oqBQeCbxGDYBCC1uCiQuDAW55lB9aWa5zNU0mzim-uqfkeFFTJ6XzBRkFdv9U3mpTPNQyoav7LWvl-fxBljiBaCkrjHVOWucBqKFXImMtZEulQg-7MBowlIjE/s320/electiovis_english.jpg" /></a></div>
<a name='more'></a>
<p>The application integrates <b>multiple Multi-Criteria Aiding methods</b> such as <i>Multi-Attribute Value Theory (MAVT)</i> and <i>Analytic Hierarchy Process (AHP)</i> and allows the decision maker to <b>choose the most suitable</b> according to the particularities of each context. This feature, in conjunction with the efforts to make the solution <b>user-friendly</b> and <b>highly interactive</b>, distinguishes <i>ElectioVis</i> from other solutions available both in the open source world and in the commercial market.</p>
<p>"Boost Your Code" started in 2011 and its 5th edition has been now held. This contest aims to support <b>scientifically challenging open source software projects</b> that are, at the same time, <b>aligned with the needs of society</b>. Every year, students and recent graduates can submit their projects for consideration, which are then analysed by a <b>jury of scientists and professionals</b>. This edition counted on the feedback of a jury made up by 12 members. Their profiles may be seen <a href="http://www.inria.fr/institut/recrutement-metiers/boost-your-code/reglement">here</a> (in French).</p>
<div class="separator" style="clear: both; text-align: center;"><a href="http://www.inria.fr/actualite/actualites-inria/palmares-de-boost-your-code-2015" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidMF4GD4daW7aEJTOplpkQcFLcFuYDSbZveBCvPOAtVjdFWypFQ_pE3m4LwzOXLQd0-oxTcioLZrBLaV7XXkujINsDo95CJAbswmY-2BRTY8iwiOZQKMuxJu_1SG4hmFmS1UwpkBdLkWte/s320/Boost+Your+Code.jpg" /></a></div>
<p>During the initial application review, the <b>four best projects are chosen as finalists</b> and their sponsors are invited to <b>audition in Paris</b>. During these auditions, candidates are requested to present their projects with a <b>multi-disciplinary audience</b> in mind. After the presentation, the members of the jury have time to ask <b>questions</b> and, afterwards, <b>deliberations</b> are held.</p>
<p>Finally, the winners are announced and INRIA offers them a <b>one-year contract</b> to continue the development of their software solution. Normally, there is only <b>one prize per year</b> but, this year, the jury decided to grant <b>two prizes</b>: <i>"Lauréat"</i> and <i>"Prix spécial du jury"</i>.
<p>I got the <i>"Prix spécial du jury"</i> and, as a result, I will be <b>joining INRIA for one year</b> so as to continue the development of <i>ElectioVis</i> and prepare for the future growth of my IT startup <b>"Idées du Sud SASU"</b>. I will keep you posted of the advancements of this project.
<p>You can read more about <i>"Boost Your Code"</i> 2015's prizes at <a href="http://www.inria.fr/actualite/actualites-inria/palmares-de-boost-your-code-2015">INRIA website</a> (in French).</p>
<p><b>Prizes will be delivered in Paris on June 13</b>, as part of the Information Technology festival <a href="http://www.inria.fr/actualite/actualites-inria/inria-a-futur-en-seine-2015">Futur en Seine 2015</a>. The ceremony will held be at 2:00 pm at the venue of the <i>"Conservatoire National des Arts et Métiers"</i> (292 Rue Saint-Martin).</p>
<p><i>Merci beaucoup INRIA !</i></p>IT Sharedhttp://www.blogger.com/profile/11763288513127086508noreply@blogger.com6Châtenay-Malabry, France48.771896 2.27074800000002648.730034499999995 2.1900670000000257 48.8137575 2.3514290000000262tag:blogger.com,1999:blog-7332477090953898587.post-72965610427923255842015-06-06T09:19:00.001+02:002015-06-19T10:52:02.537+02:00The Four Fundamental Subspaces<h1 id="the-four-fundamental-subspaces">The Four Fundamental Subspaces</h1>
<p>This is a first blog post in the series <a href="http://www.itshared.org/2015/06/the-fundamental-theorem-of-linear.html">“Fundamental Theorem of Linear Algebra”</a>, where we are working through Gilbert Strang’s paper <a href="https://scholar.google.com/scholar?cluster=7422674465482742075&hl=ru&as_sdt=0,5">“The fundamental theorem of linear algebra”</a> published by American Mathematical Monthly in 1993. </p>
<p>In this post, we will go through the first two parts of the Fundamental Theorem: the <b>dimensionality</b> and the <b>orthogonality</b> of the Fundamental Subspaces. </p>
<p></p><center><img src="http://habrastorage.org/files/3b0/9ba/cea/3b09bacea4eb4987a900c4ab7d65114f.png" alt="" title=""></center> <br>
<center><small>Original Strang’s Diagram from the paper.</small></center> <br>
<a name='more'></a><p></p>
<p>But first, let’s start with defining the notation we will use. Greek lower case letter $\alpha, \beta, \ ...$ are used for scalars, bold letters $\mathbf b, \mathbf x, \ ...$ – vectors, lowercase indexed letters $x_1, x_2, \ ...$ – components of vectors, capital letters $A$ – matrices, indexed bold letters $\mathbf a_1, \mathbf a_2, \ ... \ , \mathbf r_1, \mathbf r_2, \ ...$ – <br>
columns or rows of a matrix. $\mathbf 0$ is a vector of appropriate dimensionality with $0$ in each component.</p>
<p> </p>
<h1 id="fundamental-subspaces">Fundamental subspaces</h1>
<p>A <em>vector space</em> is a set where we have addition and scalar multiplication operators (refer to <a href="http://en.wikipedia.org/wiki/Vector_space">wikipedia</a> for more details). A <em>subspace</em> is a subset of some vector space such that it’s closed under addition and scalar multiplication, i.e. given some subspace $S$, if $\mathbf x, \mathbf y \in S$ then for any $\alpha, \beta \in \mathbb R$, $(\alpha \mathbf x + \beta \mathbf y) \in S$.</p>
<p>Let $A$ be a matrix with $m$ rows and $n$ columns. Then there are four fundamental subspaces for $A$:</p>
<ul>
<li>$C(A)$: the <em>column space</em> of $A$, it contains all linear combinations of the columns of $A$</li>
<li>$C(A^T)$: the <em>row space</em> of $A$, it contains all linear combinations of the rows of $A$ (or, columns of $A^T$)</li>
<li>$N(A)$: the <em>nullspace</em> of $A$, it contains all solutions to the system $A \mathbf x = \mathbf 0$</li>
<li>$N(A^T)$: the <em>left nullspace</em> of $A$, it contains all solutions to the system $A^T \mathbf y = \mathbf 0$.</li>
</ul>
<p> </p>
<p><strong>Example</strong>. Consider a matrix $A = \begin{bmatrix} 1 & 1 & 1 \\ 1 & 2 & 3 \\ 2 & 3 & 4 \end{bmatrix}$</p>
<p>The column space $C(A)$ is formed by all linear combinations of columns of $A$: i.e. it is $\alpha_1 \begin{bmatrix} 1 \\ 1 \\ 2 \end{bmatrix} + \alpha_2 \begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix} + \alpha_3 \begin{bmatrix} 1 \\ 3 \\ 4 \end{bmatrix}$ with all possible choices of $(\alpha_1, \alpha_2, \alpha_3)$.</p>
<p>The row space $C(A^T)$ is formed by linear combinations of rows of $A$: $\beta_1 \begin{bmatrix} 1 \\ 1 \\ 1 \end{bmatrix} + \beta_2 \begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix} + \beta_3 \begin{bmatrix} 2 \\ 3 \\ 4 \end{bmatrix}$ for all possible choices of $(\beta_1, \beta_2, \beta_3)$. </p>
<p>We will see examples for $N(A)$ and $N(A^T)$ a bit later.</p>
<p> </p>
<h2 id="dimensionality">Dimensionality</h2>
<p>The first part of the theorem talks about the dimensionality of these subspaces. It states the following:</p>
<ul>
<li>$\operatorname{dim} C(A) = \operatorname{dim} C(A^T) = r$, where $r$ is the rank of $A$,</li>
<li>$\operatorname{dim} C(A^T) + \operatorname{dim} N(A) = n$ and</li>
<li>$\operatorname{dim} C(A) + \operatorname{dim} N(A^T) = m$</li>
</ul>
<p>It means that </p>
<ul>
<li>$\operatorname{dim} C(A) = \operatorname{dim} C(A^T) = r$,</li>
<li>$\operatorname{dim} N(A) = n - r$ and</li>
<li>$\operatorname{dim} N(A^T) = m - r$</li>
</ul>
<p>How can we see that? </p>
<p>Let’s continue with the example and find the nullspace of $A = \begin{bmatrix} 1 & 1 & 1 \\ 1 & 2 & 3 \\ 2 & 3 & 4 \end{bmatrix}$.</p>
<p>First, we notice that the third row of $A$ is a sum of the first one and the second one. Thus, the dimensionality of the row space $C(A^T)$ is 2, and it means that the dimensionality of the nullpace $N(A)$ must be $m - r = 3-2=1$. </p>
<p>Let’s check it: apply <a href="http://en.wikipedia.org/wiki/Gaussian_elimination">Gaussian elimination</a> to transform $A$ to the <a href="http://en.wikipedia.org/wiki/Row_echelon_form">Row-Reduced Echelon Form</a> (“RREF” for short).</p>
<p>A matrix is in the echelon form when the leading zero elements of each row form a “staircase”:</p>
<p></p><center><img src="http://habrastorage.org/files/fd6/13b/c5b/fd613bc5b9354ddd8856d5ee1a30cfeb.png" alt="" title=""></center><p></p>
<p>The first non-zero element of each row is called <em>pivot</em>, and the corresponding columns are called <em>pivot columns</em>. In RREF, the pivot elements must be equal to $1$ and all other elements of the pivot column must be equal to $0$. Other non-pivot columns are called <em>free columns</em>. </p>
<p>RREF is typically obtained via Gaussian Elimination: you can subtract a multiple of a row from any other row and also you can multiply any row by a scalar until your matrix is in RREF.</p>
<p>If you have Matlab, you can calculate RREF as follows: </p>
<pre><code>R = rref(A)
</code></pre>
<p>Let’s get back to our matrix $A = \begin{bmatrix} 1 & 1 & 1 \\ 1 & 2 & 3 \\ 2 & 3 & 4 \end{bmatrix}$ and its RREF is $R = \begin{bmatrix} 1 & 0 & -1 \\ 0 & 1 & 2 \\ 0 & 0 & 0 \end{bmatrix}$</p>
<p>There are two pivot variables, let’s these variables $x_1$ and $x_2$. Also, there is one free variable $x_3$ that doesn’t have a pivot: it has $0$ on this position. </p>
<p>The free variable can take any value, for example, we can assign $x_3 = 1$, so the solution to $A \mathbf x = \mathbf 0$ is $x_n = \begin{bmatrix} x_1 \\ x_2 \\ 1 \end{bmatrix}$. Now we solve the system and obtain $x_1 = 1, x_2 = -2$, so the solution is $x_n = \begin{bmatrix} 1 \\ -2 \\ 1 \end{bmatrix}$. </p>
<p>There is nothing special about the choice $x_3 = 1$, so instead we can choose $x_3 = \alpha$, and obtain $x_n = \begin{bmatrix} \alpha \\ -2 \alpha \\ \alpha \end{bmatrix} = \alpha \begin{bmatrix} 1 \\ -2 \\ 1 \end{bmatrix}$. All possible choices of $\alpha$ form the nullspace $N(A)$.</p>
<p>So the nullspace of $A$ is a line.</p>
<p> </p>
<h2 id="orthogonality">Orthogonality</h2>
<p>The second part of the theorem says that the row space $C(A^T)$ and the nullspace $N(A)$ of $A$ are orthogonal and that the column space $C(A)$ and the left nullspace $N(A^T)$ are also orthogonal.</p>
<p>Two vectors are <em>orthogonal</em> if their dot product is $0$. If all vectors of one subspace are orthogonal to all vectors of another subspace, these subspaces are called <em>orthogonal</em>.</p>
<p>It is easy to see:</p>
<ul>
<li>Consider the system $A \mathbf x = \mathbf 0$</li>
<li>Let’s write $A$ as rows: $$\begin{bmatrix} — (\text{row 1}) \,— \\ — (\text{row 2}) \,— \\
\vdots \\
— (\text{row $n$}) \,—
\end{bmatrix} \cdot \mathbf x = \begin{bmatrix}
0 \\ 0 \\ \vdots \\ 0
\end{bmatrix}$$</li>
<li>You can convince yourself that it’s the same as writing $$\begin{bmatrix}
(\text{row 1})^T \mathbf x = 0 \\
(\text{row 2})^T \mathbf x = 0 \\
\vdots \\
(\text{row $n$})^T \mathbf x = 0 \\
\end{bmatrix}$$</li>
<li>Thus every row of $A$ is orthogonal to this $\mathbf x$. </li>
<li>Since$A \mathbf x = \mathbf 0$, this $\mathbf x$ belongs to the nullspace $N(A)$, and it means that every $\mathbf x \in N(A)$ is orthogonal to any row of $A$.</li>
<li>Now let’s consider some linear combinations of rows, e.g. $\big( \alpha (\text{row 1}) + \beta (\text{row 2}) \big)^T \mathbf x$, then $\alpha (\text{row 1})^T \mathbf x + \beta (\text{row 2})^T \mathbf x = \alpha \mathbf 0 + \beta \mathbf 0 = \mathbf 0$</li>
<li>So any linear combination of rows of $A$ (i.e. any vector from the row space $C(A^T)$) is orthogonal to any vector from the nullspace $N(A)$.</li>
<li>It means that $C(A^T)$ and $N(A)$ are orthogonal.</li>
</ul>
<p>The same is true for $C(A)$ and $N(A^T)$: all we have to do is to transpose the matrix $A$ and come to the same conclusion.</p>
<p> </p>
<p>If two spaces are orthogonal and they together span the entire vector space, they are called <em>orthogonal complements</em>. $C(A^T)$ and $N(A)$ are <br>
orthogonal complements, and the same is true for $C(A)$ and $N(A^T)$. We can illustrate this with a picture: </p>
<p></p><center><img src="http://alexeygrigorev.com/projects/imsem-ws14-lina/img-svg/diagram0.svg" alt="" title=""></center><p></p>
<p>On the left part of the diagram we have the row space $C(A^T)$ and the nullspace $N(A)$. They are orthogonal, meet only in the origin, and they together span the space $\mathbb R^n$. On the right we have $C(A)$ and $N(A^T)$: they are also orthogonal and they together span $\mathbb R^m$.</p>
<p> </p>
<h1 id="solution-to-a-mathbf-x-mathbf-b">Solution to $A \mathbf x = \mathbf b$</h1>
<p>Now we can use the first two parts of the theorem and develop some intuition about the system $A \mathbf x = \mathbf b$ and the crucial role the subspaces play in it. </p>
<p> </p>
<h2 id="row-space-solution">Row Space Solution</h2>
<p>The <em>general</em> solution to the system is $\mathbf x = \mathbf x_p + \mathbf x_n$, where $\mathbf x_p$ is some solution to the system, and $\mathbf x_n$ is the <em>homogenous</em> (nullspace) solution to $A \mathbf x = \mathbf 0$. It works because $A \mathbf x = A (\mathbf x_p + \mathbf x_n) = A \mathbf x_p + A \mathbf x_n = \mathbf b + \mathbf 0 = \mathbf b$.</p>
<p>Since $C(A^T)$ and $N(A)$ are orthogonal complements, they span the entire space $\mathbb R^n$ and every vector $\mathbf x$ from $\mathbb R^n$ can be expressed as $\mathbf x = \mathbf x_r + \mathbf x_n$ such that $\mathbf x_r$ is from the row space $C(A^T)$ and $\mathbf x_n$ is from the nullspace $N(A)$.</p>
<p>There are many possible choices of $\mathbf x_p$, but among them only one of them belongs to the row space. We call it the <em>row space solution</em> $\mathbf x_r$ to the system.</p>
<p>But sometimes there is no solution. A solution exists only if the right-side $\mathbf b$ belongs to the column space $C(A)$, i.e. when $\mathbf b$ is a linear combination of columns of $A$.</p>
<p>Let’s put it more formally: if $\mathbf a_i$ are the columns of $A$, i.e. $A = \left[ \mathop{\mathbf a_1}\limits_|^| \, \mathop{\mathbf a_2}\limits_|^| \ \cdots \ \mathop{\mathbf a_n}\limits_|^| \right]$, then the solution exists if we can find coefficients $(x_1 , \ ... \ , x_n)$ such that $x_1 \mathbf a_1 + x_2 \mathbf a_2 + \ ... \ + x_n \mathbf a_n = \mathbf b$. If we can, then they form a solution $\mathbf x = \begin{bmatrix} x_1 \\ \vdots \\ x_n \end{bmatrix}$. </p>
<p>You can convince yourself that writing $x_1 \mathbf a_1 + x_2 \mathbf a_2 + \ ... \ + x_n \mathbf a_n = \mathbf b$ is the same as writing $A \mathbf x = \mathbf b$.</p>
<p>We can illustrate this with a diagram:</p>
<p></p><center><img src="http://alexeygrigorev.com/projects/imsem-ws14-lina/img-svg/diagram1.svg" alt="" title=""></center><p></p>
<p>In this diagram $\mathbf b$ is in the column space $C(A)$, so there is a solution $\mathbf x$ to the system $A \mathbf x = \mathbf b$. This solution $\mathbf x$ can be expressed as $\mathbf x_r + \mathbf x_n$ such that $\mathbf x_r$ belongs to the row space $C(A^T)$ and $\mathbf x_n$ is from the nullspace $N(A)$. We know that $A \mathbf x_r = \mathbf b$ and $A \mathbf x = \mathbf b$, and we can show that with arrows from $\mathbf x_r$ and $\mathbf x$ to $\mathbf b$.</p>
<p>Let’s get back to our example: consider a system $A \mathbf x = \mathbf b$ with $A = \begin{bmatrix} 1 & 1 & 1 \\ 1 & 2 & 3 \\ 2 & 3 & 4 \end{bmatrix}$ and $\mathbf b = \begin{bmatrix} 0 \\ 1 \\ 1 \end{bmatrix}$.</p>
<p>We already know that the column space is $\alpha_1 \begin{bmatrix} 1 \\ 1 \\ 2 \end{bmatrix} + \alpha_2 \begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix} + \alpha_3 \begin{bmatrix} 1 \\ 3 \\ 4 \end{bmatrix}$. Now to check if the system has a solution, we need to show that $\mathbf b \in C(A)$. Or, in other words, we need to show that it is possible to find such $\mathbf x = \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix}$ that $x_1 \begin{bmatrix} 1 \\ 1 \\ 2 \end{bmatrix} + x_2 \begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix} + x_3 \begin{bmatrix} 1 \\ 3 \\ 4 \end{bmatrix} = \begin{bmatrix} 0 \\ 1 \\ 1 \end{bmatrix}$.</p>
<p>In this example it is possible: $x_1 = -1, x_2 = 1$ and $x_3 = 0$. So we have $-1 \begin{bmatrix} 1 \\ 1 \\ 2 \end{bmatrix} + 1 \begin{bmatrix} 1 \\ 2 \\ 3 \end{bmatrix} + 0 \begin{bmatrix} 1 \\ 3 \\ 4 \end{bmatrix} = \begin{bmatrix} 0 \\ 1 \\ 1 \end{bmatrix}$.</p>
<p>Not only have we established that $\mathbf b \in C(A)$, but also found a solution $\mathbf x_p = \begin{bmatrix} x_1 \\ x_2 \\ x_3 \end{bmatrix} = \begin{bmatrix} 1 \\ -1 \\ 0 \end{bmatrix}$. This solution is not necessarily the row space solution, i.e. it may not belong to the row space $C(A^T)$.</p>
<p>The nullspace $N(A)$ contains all the solutions to $A \mathbf x = \mathbf 0$. We already know how to find the nullspace solution, and for this example it’s $x_n = \begin{bmatrix} \alpha \\ -2 \alpha \\ \alpha \end{bmatrix} = \alpha \begin{bmatrix} 1 \\ -2 \\ 1 \end{bmatrix}$. </p>
<p>The complete solution $\mathbf x$ is a sum of some solution $\mathbf x_p $ and the homogenous solutions $\mathbf x_n$: <br>
$\mathbf x = \mathbf x_p + \mathbf x_n = \begin{bmatrix} 0 \\ 1 \\ 1 \end{bmatrix} + \alpha \begin{bmatrix} 1 \\ -2 \\ 1 \end{bmatrix}$. Note that the set of all solutions $\mathbf x$ is just a shifted nullspace $N(A)$.</p>
<p>We can illustrate it with following picture:</p>
<p></p><center><img src="http://alexeygrigorev.com/projects/imsem-ws14-lina/img-svg/row-space-x-special3.svg" alt="" title=""></center><p></p>
<p>Because the rank of $A$ is two, the row space $C(A^T)$ contains only two linearly independent vectors, so it is a plane in $\mathbb R^3$ formed by two rows $\mathbf r_1$ and $\mathbf r_2$. The row space solution $\mathbf x_r$ also belongs to $C(A^T)$, but the solution $\mathbf x_p = \begin{bmatrix} 1 \\ -1 \\ 0 \end{bmatrix}$ does not. </p>
<p>Also, maybe this animation will be helpful:</p>
<center>
<video poster="//i.imgur.com/R8QFjOKh.jpg" preload="auto" autoplay="autoplay" muted="muted" loop="loop" webkit-playsinline>
<source src="//i.imgur.com/R8QFjOK.webm" type="video/webm">
<source src="//i.imgur.com/R8QFjOK.mp4" type="video/mp4">
</video>
</center>
<p>Click <a href="http://i.imgur.com/R8QFjOK.gifv">here</a> if it doesn’t play.</p>
<p>Here we draw the row space, the nullspace and the solution set, and we just rotate it to see what’s going on (otherwise it’s hard to understand a 3D picture in 2D). Here the plane is the row space, the black dashed line is the nullspace, and the red dashed line is the solution set. We see that the solution space and the row space intersect in one special point, and this point is the special solution $\mathbb x_r$.</p>
<p>Let’s freeze it and look at one particular angle:</p>
<p></p><center><img src="http://alexeygrigorev.com/projects/imsem-ws14-lina/img-svg/3dpic-31.svg" alt="" title=""></center><p></p>
<p>In this 2D projection we don’t see the row space altogether, so it looks like its two basis vectors are lying on the line. Then we have the nullspace, which is orthogonal to the row space, with one basis vector. Finally, we have a solution set (dotted red line) and one particular solution (the red arrow). The row space solution $\mathbf x_r$ is on the intersection between the solution set and the row space (the black arrow). </p>
<p>You are probably wondering how we can obtain the row space solution $\mathbf x_r$. To do this, at first we need to recognize that it’s a projection of $\mathbf x_p$ onto the row space $C(A^T)$. We will see how to find this projection in the next post of this series: “The Least Squares: The Projection onto Subspaces”. <!-- <a href="http://www.itshared.org/2015/06/the-least-squares-projection-onto.html">The Least Squares: The Projection onto Subspaces</a>. --></p>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com2tag:blogger.com,1999:blog-7332477090953898587.post-22940113188229690472015-06-06T08:49:00.000+02:002015-06-19T10:52:13.968+02:00The Fundamental Theorem of Linear Algebra by G. Strang<h1 id="the-fundamental-theorem-of-linear-algebra">The Fundamental Theorem of Linear Algebra</h1>
<p></p><center><a href="http://www-math.mit.edu/~gs/"><img src="http://habrastorage.org/files/a2b/4d7/a13/a2b4d7a131ab4a58a4b3dcae1b0a930a.jpg" alt="" title=""></a></center><p></p>
<p>This is a series of articles devoted to Gilbert Strang’s Paper <a href="https://scholar.google.com/scholar?cluster=7422674465482742075&hl=ru&as_sdt=0,5">“The fundamental theorem of linear algebra”</a> published by American Mathematical Monthly in 1993.</p>
<a name='more'></a>
<p>The paper describes the “Strang’s diagram”, a diagram that shows actions of <script id="MathJax-Element-61" type="math/tex">A</script>, an <script id="MathJax-Element-62" type="math/tex">m \times n</script> matrix, as linear transformations from the space <script id="MathJax-Element-63" type="math/tex">\mathbb R^m</script> to <script id="MathJax-Element-64" type="math/tex">\mathbb R^n</script>. The diagram helps to understand the fundamental concepts of Linear Algebra in terms of the four subspaces by visually illustrating the actions of <script id="MathJax-Element-65" type="math/tex">A</script> on all these subspaces.</p>
<p></p><center><img src="http://alexeygrigorev.com/projects/imsem-ws14-lina/img-svg/diagram1.svg" alt="" title=""></center><p></p>
<p>In this series of blog posts we will work through the paper and will try to understand it.</p>
<p>There are 4 parts of the Fundamental theorem of Linear Algebra:</p>
<ol>
<li>The dimensions of the subspaces;</li>
<li>The orthogonality of the subspaces;</li>
<li>The basis vectors are orthogonal;</li>
<li>The matrix with respect to these bases is orthogonal.</li>
</ol>
<p>So there will be 4 blog posts based based on the paper:</p>
<ol>
<li><a href="http://www.itshared.org/2015/06/the-four-fundamental-subspaces.html">The Four Fundamental Subspaces</a> (theorem part 1 and part 2)</li>
<li>The Least Squares: Projection onto Subspaces (theorem part 2)</li>
<!--<li><a href="http://www.itshared.org/2015/06/the-least-squares-projection-onto.html">The Least Squares: Projection onto Subspaces</a> (theorem part 2)</li> -->
<li>Singular Value Decomposition (theorem parts 3 and 4)</li>
<li>Pseudoinverse (theorem parts 3 and 4)</li>
</ol>
<p>This series of blog posts is based on the report I wrote for the “Hot Topics in Information Management” course taught by Sebastian Schelter and Asterios Katsifodimos at TU Berlin in the winter semester of 2014-2015. During the course I was mentored by Moritz Schubotz.</p>
<p>You can access the pdf of my report at <a href="http://alexeygrigorev.com/projects/imsem-ws14-lina/imsem.pdf">http://alexeygrigorev.com/projects/imsem-ws14-lina/imsem.pdf</a>.</p>
<p>The course is a part of the IT4BI program I participate in, and if you would like to learn more about this program, you can check posts tagged with <a href="http://www.itshared.org/search/label/IT4BI">“IT4BI”</a> on this website. For the detailed description of classes we took at TU Berlin, see the post <a href="http://www.itshared.org/2015/01/it4bi-distributed-and-large-scale.html">“IT4BI: Distributed and Large-Scale Business Intelligence”</a>.</p>
<p>Sources:</p>
<ul>
<li>Strang, Gilbert. “The fundamental theorem of linear algebra.” American Mathematical Monthly (1993): 848-855. (<a href="https://scholar.google.com/scholar?cluster=7422674465482742075&hl=ru&as_sdt=0,5">pdf</a>)</li>
<li>Strang, Gilbert. “The Four Fundamental Subspaces: 4 Lines.” (<a href="http://web.mit.edu/~linalg/webpage/Essays/newpaper_ver3.pdf">pdf</a>)</li>
<li>Strang, Gilbert. Linear Algebra and Its Applications. Brooks Cole, 1988. </li>
<li>Strang, Gilbert. <a href="http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm">Linear Algebra course</a> at MIT OpenCourseWare</li>
</ul>
<p>Also you can see our post <a href="http://www.itshared.org/2015/02/best-time-to-learn-linear-algebra-is-now.html">“Best Time to Learn Linear Algebra is Now!”</a> for other good Linear Algebra resources. </p>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com1tag:blogger.com,1999:blog-7332477090953898587.post-14316707508603191022015-04-29T15:28:00.000+02:002015-04-30T11:09:34.537+02:00How to count distinct?<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiO6JU938HimbpHtHhqWDKRKnAYvdAbccSC8ltaPNrNLjGEMcg9at3x76cqZcBJoFh_bv_vV-I9KkiNE_mwgVSg5gb9GZ41A7GawCSiFoEIvcUoc6n1jCGB_78vlqs_ROgYwOoovvJp1Is/s1600/COUNTING_TO_TWO.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiO6JU938HimbpHtHhqWDKRKnAYvdAbccSC8ltaPNrNLjGEMcg9at3x76cqZcBJoFh_bv_vV-I9KkiNE_mwgVSg5gb9GZ41A7GawCSiFoEIvcUoc6n1jCGB_78vlqs_ROgYwOoovvJp1Is/s1600/COUNTING_TO_TWO.jpg" height="220" width="320" /></a></div>
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Counting the number of distinct elements in a data set is a very common query. It can help give you an idea of how many duplicates you are dealing with. Let's say for example that you have a set of transactions, and you wish to detect if these transactions <span class="message_content">are either associated to a small set of frequent buying customers or performed by different customers</span>. This can help you understand your clients and what type of marketing strategies you need to adopt. <br /> <a name='more'></a>
<br />
Counting the number of distinct elements in a set is an easy task if you're dealing with a small data set. With a small enough set, a computer would calculate count distinct the same way a human would, simply by going over the elements in the set, one by one, inserting each new element in memory, and discarding any element that has already been seen before. When all the elements have been checked, the size of the set which is stored in memory is the count distinct.<br />
<br />
However, you may have already noticed that the previous strategy has a shortcoming. What if your dataset (or at least the number of distinct elements) does not fit into memory? One way to decrease memory requirements would be to use a bitmap (array of "1's" and "0's") rather than a hash structure to track the elements that have been seen. In a bitmap, you only require one bit for storing every element, reducing memory significantly. The idea is that every bit in your bitmap can be mapped to a different element. This strategy requires the use of a hash function, that is, a function that can map all incoming elements to a different bit in the bitmap. The bitmap is originally set with "0's", and when a new element comes in and is mapped to a given bit, this bit is set to one. If you wish to know the count distinct you only need to count the number of bits set to "1" (provided the hash function can guarantee that it maps all elements to a different bit). Even though bitmaps considerably reduce memory requirements, it still requires memory which is linear with respect to the size of the data set. This is a problem whenever you are dealing with very large datasets.<br />
<br />
If you are dealing with a dataset that is already stored on disk, an alternative to counting distinct elements is to start by sorting the elements in the dataset. If the elements are sorted, it is guaranteed that duplicate elements will sit next to each other. The following step is to go over the sorted set of elements, increasing the count distinct counter every time a new element is seen, and discarding all subsequent duplicate elements. The only memory requirement for this strategy is for the count distinct counter, plus one element of the dataset at a time. Basically, there are no limitations in terms of main memory requirements. However, this approach requires first making a complete scan over your dataset, materializing the sorted data set onto disk (since we are assuming the entire set does not fit in main memory), and finally making a second complete scan of the sorted data. In terms of processing times, this is a very expensive task. Moreover, having access to your complete data set before being able to analyse it is a strong restriction. If you are dealing with data that is continuously growing, and you wish to monitor the number of distinct elements in time, it is not practical to have to store the entire data set, and perform two complete scans over it every time you wish to know how the count distinct has evolved. In such a context it makes much more sense to process each incoming element, and use it to continuously update your count distinct statistic. Processing incoming data as it arrives, in order to update queries is known as <a href="http://en.wikipedia.org/wiki/Data_stream_mining" target="_blank">data stream processing</a> (or data stream mining), and is becoming increasingly popular, since nowadays data is being generated at very high speed. <br />
<br />
Now you might wonder, how can we update our count distinct by processing only incoming elements, but without storing all elements that we have seen in memory? Actually, counting the number of distinct elements in a data set, by performing only one scan of the data, and using memory which is sub-linear in the size of the data set, is impossible. Luckily for us, probabilities come to the rescue. Researchers have come up with algorithms that allow us to ESTIMATE the number of distinct elements, following the restrictions stated above. Now, in the context of data streaming, an estimation can get us pretty far, since we are generally not interested in the exact count distinct, but rather a notion of it, to understand incoming data.<br />
<br />
Though there is more than one algorithm designed for estimating count distinct on data streams, I am only going to introduce you to <a href="http://en.wikipedia.org/wiki/HyperLogLog" target="_blank">HyperLogLog</a> since this is one of the most popular ones. This algorithm was proposed in 2007 by <a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.9475" target="_blank">Flajolet et.al.</a> The idea behind the algorithm is simple. The probability of observing k ”0” bits at the beginning of a string of bits, is $(1/2)^k$. So, if you have a hash function that can map any incoming element to an array of bits, by tracking the maximum number of leading ”0’s”, we can estimate the number of distinct elements. If the maximum number of leading "0's" is m, we can estimate that we have processed $(2^m)$ distinct elements. Of course, there is bias in this estimation, especially when the number of elements that has been tracked is small. Imagine that you have only seen 5 elements, but by bad luck, one of the elements mapped to an array with 10 leading "0's". The bias is mitigated by separating the incoming elements into buckets, and averaging over the estimation of each bucket (stochastic averaging). The following image shows the processing of new elements in HyperLogLog. An input element is mapped to an array of bits, the first bits are used to select the bucket to which the element will be assigned. After assigning the element to a bucket (and removing the bits used for that purpose), the number of leading "0's" are counted. If it is larger than the current bucket maximum, then the bucket maximum is updated. In the end, count distinct is estimated by the mean over $2^{M[i]}$.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQxhCjvoLjGHAC-MTOJlmfPTcqa0mjHtFBNW0XMeSdVNTiZlxXNDOdxU6W5xLK-IaAV_SN15g1NGGiup3N7M_cRVt8sJ6_5krDaWFldH44N9x5ZSPz5YBW2D2ytcy4DrDHojPjPenWXzU/s1600/HyperLogLog.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQxhCjvoLjGHAC-MTOJlmfPTcqa0mjHtFBNW0XMeSdVNTiZlxXNDOdxU6W5xLK-IaAV_SN15g1NGGiup3N7M_cRVt8sJ6_5krDaWFldH44N9x5ZSPz5YBW2D2ytcy4DrDHojPjPenWXzU/s1600/HyperLogLog.jpg" height="161" width="400" /></a></div>
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As its name implies, HyperLogLog only requires memory that is $O(\log_2 \log_2 n)$ where $n$ is the cardinality of the data set. Another very nice property of HyperLogLog, is that estimates from two different data sets can be merged, without affecting the precision of the estimation. As you can imagine, if you have two data sets, one with an estimated count distinct of $10^{5}$, and another data set with an estimated count distinct of $10^{6}$, you cannot say that the count distinct of merging both data sets will be the sum of the individual count distincts. However, with HyperLogLog, if both datasets have used the same hash function for estimation, merging both results can be done by merging the buckets of each dataset, that is, selecting the maximum number of leading "0's" of each bucket, and finally calculating count distinct the way it is done for a single dataset. </div>
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If you are interested in putting this and other data streaming algorithms into practice, the people at Clearspring have made a very nice <a href="https://github.com/addthis/stream-lib" target="_blank">java library</a> available on github. They have also written a nice <a href="http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html" target="_blank">post</a> on Count Distinct.</div>
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Anonymoushttp://www.blogger.com/profile/09844024931548619141noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-37141300468000803372015-04-23T11:03:00.001+02:002015-06-19T10:52:23.278+02:00Apache Mahout Samsara: The Quick Start <h1 id="apache-mahout-samsara-the-quick-start">Apache Mahout Samsara: The Quick Start </h1>
<p></p>
<a href="https://mahout.apache.org/"><center><img src="http://habrastorage.org/files/acf/77c/9c5/acf77c9c55b94e42b0ed2ecd2435c70a.png" alt="" title=""></center></a>
<p></p>
<p>Last week the newest <a href="https://mahout.apache.org/">Apache Mahout</a> 0.10 <a href="http://mail-archives.us.apache.org/mod_mbox/www-announce/201504.mbox/%3CCAOtpBjhD7kG_JSdTkZDQwrQSh7xwMnnQqUYMzp-Ct5qFSBe=GQ@mail.gmail.com%3E">was released</a>. One of the new features it has is a new math environment called “Samsara”, or Mahout Scala/Spark Bindings. </p>
<p>Samsara is a Linear Algebra library for Mahout. It’s written in Scala, which makes it possible to use operator overloading and it features nice R-like or Matlab-like syntax for basic Linear Algebra operations. For example, matrix multiplication is just <code>X %*% Y</code>. What is more, these operations can be distributed and run by an executing environment - currently by Apache Spark. </p>
<p>In this article we will see how to quickly set up a basic skeleton project and then we’ll try to do some very simple analysis on a 200 MB dataset. </p>
<a name='more'></a>
<p>We will use: </p>
<ul>
<li>Java 1.7, Scala 2.10.4 </li>
<li>Scala IDE or any other IDE with a Scala plugin</li>
<li>Apache Maven</li>
<li>Apache Mahout and Apache Spark</li>
</ul>
<p>The code is available on <a href="https://github.com/alexeygrigorev/itshared-howto/tree/master/mahout-spark-binding">github</a>.</p>
<h1 id="preliminaries">Preliminaries</h1>
<p>This article is based on and inspired by the article <a href="https://mahout.apache.org/users/sparkbindings/play-with-shell.html">Playing with Mahout’s Spark Shell</a>, so you may want to read it before continuing. Also, the <a href="https://mahout.apache.org/users/sparkbindings/ScalaSparkBindings.pdf">Scala and Spark Bindings manual</a> gives a good overview of the syntax. </p>
<p>Before we start, let me introduce you to a couple of concepts that we will use throughout this post.</p>
<ul>
<li>RDD, which stands for “Resilient Distributed Dataset”, is the basic abstraction for Apache Spark data flows. You can read about them in Spark official documentation, for example, in the official <a href="https://spark.apache.org/docs/latest/quick-start.html">Quick Start guide</a>.</li>
<li>DRM, which stands for “Distributed Row Matrix”, is the main abstraction for matrices in Apache Mahout: these are matrices that are distributed and stored by rows. DRMs can be backed by Spark’s RDDs or anything else, e.g. Apache Hadoop or Apache Flink. </li>
<li>in-core matrices/vectors - it’s the “opposite” of DRM: they are usual matrices or vectors that are kept in the memory of JVM, they are not distributed. </li>
</ul>
<p>DRMs could be of two types: one row at a time or by blocks (“blockified”). Let’s illustrate this. Suppose we have the following matrix:</p>
<p><script id="MathJax-Element-1" type="math/tex; mode=display">\mathbf X = \begin{bmatrix}
2 & 2 & 10.5 & 10 \\
1 & 2 & 12 & 12 \\
1 & 1 & 12 & 13 \\
2 & 1 & 11 & 13 \\
1 & 2 & 12 & 11 \\
2 & 1 & 16 & 8 \\
6 & 2 & 17 & 1 \\
3 & 2 & 13 & 7 \\
3 & 3 & 13 & 4 \\
\end{bmatrix}</script></p>
<p>If we index it by rows, and then store one row at a time, we will have the following representation: </p>
<p></p><center><img src="http://habrastorage.org/files/d75/076/3ad/d750763ad90840f7904680e92e42fe5c.png" alt="" title=""></center><p></p>
<p>So, here it’s a distributed collection of <code>(Int, Vector)</code> pairs. </p>
<p>But we can also take blocks of the matrix - i.e. several rows at a time:</p>
<p></p><center><img src="http://habrastorage.org/files/7bc/a99/e55/7bca99e55fa64a59911218f934e84c55.png" alt="" title=""></center><p></p>
<p>In this representation, it’s a distributed collection of <code>(Array[Int], Matrix)</code> pairs. </p>
<p>Under the hood, Mahout usually operates on DRMs of the second type, i.e. “blockified” DRMs because typically it’s more efficient. </p>
<h2 id="math">Math </h2>
<p>Let’s very briefly review some math we need for this article. </p>
<p>First, we will go over the basics of <a href="http://en.wikipedia.org/wiki/Ordinary_least_squares">Ordinary Least Squares</a> (OLS) Regression. In short, for OLS we are given a dataset <script id="MathJax-Element-2" type="math/tex">\mathcal D = \{ (\mathbf x_i, y_i) \}</script>, where <script id="MathJax-Element-3" type="math/tex">\mathbf x_i</script> are vectors of observed “predictors” or features and <script id="MathJax-Element-4" type="math/tex">y_i</script> are observed values (“responses”). We put all <script id="MathJax-Element-5" type="math/tex">\mathbf x_1, \ ... \ , \mathbf x_N</script> as rows into a matrix <script id="MathJax-Element-6" type="math/tex">\mathbf X</script> (sometimes called the “design” or “data” matrix) and all <script id="MathJax-Element-7" type="math/tex">y_i</script> in a vector <script id="MathJax-Element-8" type="math/tex">\mathbf y = (y_1, \ ... \ , y_N)</script> of observed values (“responses”). The goal is to find <script id="MathJax-Element-9" type="math/tex">\hat{\mathbf w}</script> such that minimizes the squared error <script id="MathJax-Element-10" type="math/tex">\| \mathbf X \hat{\mathbf w} - \mathbf y \|^2</script>.</p>
<p>This problem can be solved with the Normal Equation: we need to find a solution to the system <script id="MathJax-Element-11" type="math/tex">\mathbf X^\mathbf T \mathbf X \hat {\mathbf w} = \mathbf X^\mathbf T \mathbf y</script>, and the solution is <script id="MathJax-Element-12" type="math/tex">\hat {\mathbf w} = (\mathbf X^\mathbf T \mathbf X)^{-1} \mathbf X^\mathbf T \mathbf y</script>.</p>
<p>For a new unseen data item <script id="MathJax-Element-13" type="math/tex">\mathbf x</script> the predicted value <script id="MathJax-Element-14" type="math/tex">\hat y</script> is <script id="MathJax-Element-15" type="math/tex">\hat y = \mathbf x^\mathbf T \, \hat{\mathbf w} = x_1 \, w_1 + \ ... \ + x_p \, w_p</script>. Note that it’s a line, and we usually should have an interception term <script id="MathJax-Element-16" type="math/tex">w_0</script>, so instead we need to write <script id="MathJax-Element-17" type="math/tex">\hat y = w_0 + x_1 \, w_1 + \ ... \ + x_p \, w_p = w_0 + \mathbf x^\mathbf T \, \hat{\mathbf w}</script>. To incorporate this intercept, we add a <em>bias term</em> <script id="MathJax-Element-18" type="math/tex">x_0 = 1</script> for all observations: i.e. we add an additional row to <script id="MathJax-Element-19" type="math/tex">\mathbf X</script> and this row contains only ones. With this we can easily write <script id="MathJax-Element-20" type="math/tex">\mathbf x^\mathbf T \, \hat{\mathbf w}</script>. </p>
<!-- You can read more about OLS Regression and Least Squares in our blogpost [The Least Squares: Projection onto Subspaces](http://www.itshared.org/2015/04/the-least-squares-projection-onto.html) -->
<p>Ridge Regression is a regularized version of the OLS regression, and instead of solving <script id="MathJax-Element-21" type="math/tex">(\mathbf X^\mathbf T \mathbf X) \, \mathbf {\hat w} = \mathbf X^\mathbf T \mathbf y</script> we solve <script id="MathJax-Element-22" type="math/tex">(\mathbf X^\mathbf T \mathbf X + \mathbf I \lambda) \, \mathbf {\hat w} = \mathbf X^\mathbf T \mathbf y</script>. </p>
<p>For Ridge Regression it is important to standardize the data (for reasons described <a href="http://stats.stackexchange.com/questions/111017/question-about-standardizing-in-ridge-regression">here</a>). Usually it’s done by applying <a href="http://en.wikipedia.org/wiki/Standard_score"><script id="MathJax-Element-23" type="math/tex">Z</script>-standardization</a>: let <script id="MathJax-Element-24" type="math/tex">\boldsymbol \mu</script> be the mean over all observations and let <script id="MathJax-Element-25" type="math/tex">\boldsymbol \sigma</script> be the standard deviation, and then each item <script id="MathJax-Element-26" type="math/tex">\mathbf x</script> is standardized as <script id="MathJax-Element-27" type="math/tex">\tilde {\mathbf x} = \cfrac{\mathbf x - \boldsymbol \mu}{\boldsymbol \sigma}</script>.</p>
<p>Lastly, <a href="http://en.wikipedia.org/wiki/Dimensionality_reduction">Dimensionality Reduction</a> is a set of techniques for transforming a high-dimensional data into some lower-dimensional representation. <a href="http://en.wikipedia.org/wiki/Principal_component_analysis">Principal Component Analysis</a> is the most popular method, and it is typically done via <a href="http://en.wikipedia.org/wiki/Singular_value_decomposition">Singular Value Decomposition</a>. It is <a href="http://stats.stackexchange.com/questions/22329/how-does-centering-the-data-get-rid-of-the-intercept-in-regression-and-pca">important to center the data</a> (i.e. subtract the mean of each column) before applying PCA. </p>
<p>For more detailed theory I would recommend to refer to two excellent books <a href="http://www-bcf.usc.edu/~gareth/ISL/">An Introduction to Statistical Learning</a> and <a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/">The Elements of Statistical Learning</a>, both freely available as pdf to download.</p>
<p>And now we’re ready to start. </p>
<h1 id="the-cereal-regression">The Cereal Regression</h1>
<p>To get started, let’s first try to reproduce the OLS regression example on the cereals dataset from the <a href="https://mahout.apache.org/users/sparkbindings/play-with-shell.html">cereals manual</a>. </p>
<p>But before that, you need to have an IDE that supports Scala. I personally prefer the Eclipse-based <a href="http://scala-ide.org/download/sdk.html">Scala IDE</a>, but any other IDE with Scala plugin should be equally good. Also, make sure you’re running Scala 2.10.x: if it’s Scala 2.11 or greater, it might not work, because some packages are built under 2.10 and might not work for the latest Scala compilers. </p>
<p>Then we need to tell Maven that we’re going to use Scala. To do this, add the following lines to your <code><plugins></code> section</p>
<pre><code><plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.1.3</version>
<executions>
<execution>
<goals>
<goal>add-source</goal>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
</code></pre>
<p>Now we can add dependencies to Mahout Math, Mahout Scala Bindings and Mahout Spark Bindings. You’ll also need Spark dependency as well</p>
<pre><code><dependency>
<groupId>org.apache.mahout</groupId>
<artifactId>mahout-math</artifactId>
<version>${mahout.math.version}</version>
</dependency>
<dependency>
<groupId>org.apache.mahout</groupId>
<artifactId>mahout-math-scala_2.10</artifactId>
<version>${mahout.math.version}</version>
</dependency>
<dependency>
<groupId>org.apache.mahout</groupId>
<artifactId>mahout-spark_2.10</artifactId>
<version>${mahout.math.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.10</artifactId>
<version>${spark.version}</version>
</dependency>
</code></pre>
<p>Additionally, add this to your <code><properties></code> section:</p>
<pre><code><mahout.math.version>0.10.0</mahout.math.version>
<spark.version>1.1.1</spark.version>
</code></pre>
<p>The latest version of Spark is 1.3.0, but the Mahout Spark Binding is built with Spark 1.1.1, so it’s better to use it, otherwise you may get crazy exceptions like “method not found” (though it should be fixed soon by <a href="https://github.com/apache/mahout/pull/117">this pull request</a>).</p>
<p>Having done this, we’re ready to start. Let’s create a scala object with the <code>main</code> method and add the following imports:</p>
<pre><code>import org.apache.mahout.math._
import org.apache.mahout.math.scalabindings._
import org.apache.mahout.math.drm._
import org.apache.mahout.math.scalabindings.RLikeOps._
import org.apache.mahout.math.drm.RLikeDrmOps._
import org.apache.mahout.sparkbindings._
</code></pre>
<p>The important imports here are:</p>
<ul>
<li><code>scalabindings._</code> adds functions like <code>eye</code>, <code>diag</code>; <code>dense</code> and <code>sparse</code> for matrix creation and <code>svec</code>, <code>dvec</code> for vector creation</li>
<li><code>RLikeOps._</code> adds r-like operators, for example <code>%*%</code> for matrix multiplication</li>
<li><code>sparkbindings._</code> for distributing the Linear Algebra operations with Spark</li>
</ul>
<p>With this, we’re ready to code. The very first thing is to set up the <code>DisributedContext</code>, in our case - Spark-based. The “right” way to initialize the Spark context is </p>
<pre><code>implicit val ctx =
mahoutSparkContext(masterUrl="local[*]", appName="mahout spark binding")
</code></pre>
<p>Here we assume you’re running locally, otherwise you need to change <code>"local[*]"</code> to your Spark server instance. </p>
<p>However using the <code>mahoutSparkContext</code> method requires additional configuration steps, like installing Mahout or building Mahout from sources and setting the <code>MAHOUT_HOME</code> environment variable. If you don’t do it, you’ll get an error. For a quick start we don’t want to build Mahout from sources, so instead we’ll use the following set up:</p>
<pre><code>val conf = new SparkConf()
.setAppName("Simple Application")
.setMaster("local[*]")
.set("spark.serializer",
"org.apache.spark.serializer.KryoSerializer")
.set("spark.kryo.registrator",
"org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator")
implicit val sc = new SparkDistributedContext(new SparkContext(conf))
</code></pre>
<p>So we configure the context manually: first, create a <code>SparkConf</code> object the way we usually do it for Spark application. The last two parameters are needed for properly serializing and deserializing Mahout’s <code>Vector</code> objects, and without it, you’ll be seeing “Failed to serialize task” errors.</p>
<p>Let’s enter the matrix with the data from the cereal manual:</p>
<pre><code>val matrix = dense(
(2, 2, 10.5, 10, 29.509541), // Apple Cinnamon Cheerios
(1, 2, 12, 12, 18.042851), // Cap'n'Crunch
(1, 1, 12, 13, 22.736446), // Cocoa Puffs
(2, 1, 11, 13, 32.207582), // Froot Loops
(1, 2, 12, 11, 21.871292), // Honey Graham Ohs
(2, 1, 16, 8, 36.187559), // Wheaties Honey Gold
(6, 2, 17, 1, 50.764999), // Cheerios
(3, 2, 13, 7, 40.400208), // Clusters
(3, 3, 13, 4, 45.811716)) // Great Grains Pecan)
</code></pre>
<p>Now we need to transform it to a DRM: </p>
<pre><code>val drmData = drmParallelize(matrix, numPartitions = 2)
</code></pre>
<p>The first 4 columns of the matrix are our matrix <script id="MathJax-Element-28" type="math/tex">\mathbf X</script>, and the last one is the vector <script id="MathJax-Element-29" type="math/tex">\mathbf y</script>. In order to extract them we use slicing operators.</p>
<pre><code>val drmX = drmData(::, 0 until 4)
val y = drmData.collect(::, 4)
</code></pre>
<p><code>0 until 4</code> gets the slice of columns from 0 to 4 (exclusive) and <code>::</code> returns all the rows. The <code>collect</code> function “materializes” the vector: it creates an in-core <code>Vector</code> back from a DRM.</p>
<p>So to solve the OLS problem we need to compute <script id="MathJax-Element-30" type="math/tex">\mathbf X^\mathbf T \mathbf X</script> and <script id="MathJax-Element-31" type="math/tex">\mathbf X^\mathbf T \mathbf y</script>: </p>
<pre><code>val drmXtX = drmX.t %*% drmX
val drmXty = drmX.t %*% y
</code></pre>
<p>Now we need to solve the system <script id="MathJax-Element-32" type="math/tex">\mathbf X^\mathbf T \mathbf X \hat {\mathbf w} = \mathbf X^\mathbf T \mathbf y</script>: </p>
<pre><code>val w = solve(drmXtX, drmXty)
println(w)
</code></pre>
<p>The output is:</p>
<pre><code>{
0 => {0:5.247349465378446}
1 => {0:2.750794578467531}
2 => {0:1.1527813010791554}
3 => {0:0.10312017617608908}
}
</code></pre>
<p>Note that <code>w</code> here is a one-column matrix, not a vector. To transform it to a vector, we use <code>w(::, 0)</code>.</p>
<p>Now, when we found the best <script id="MathJax-Element-33" type="math/tex">\hat{\mathbf w}</script>, we can use it to calculate <script id="MathJax-Element-34" type="math/tex">\hat {\mathbf y}</script> - values the model predict for each <script id="MathJax-Element-35" type="math/tex">\mathbf x_i</script>:</p>
<pre><code>val yFitted = (drmX %*% w).collect(::, 0)
println(yFitted)
</code></pre>
<p>And we see the output</p>
<pre><code>{0:29.131693510783975,
1:25.819756349376444,
2:23.172081947084997,
3:27.266650111384287,
4:25.716636173200357,
5:32.514955735899626,
6:56.68608824372747,
7:36.95163570033205,
8:39.393069750271316}
</code></pre>
<p>Let’s wrap this into functions:</p>
<pre><code>def ols(drmX: DrmLike[Int], y: Vector) = {
val sol = solve(drmX.t %*% drmX, drmX.t %*% y)
sol(::, 0)
}
</code></pre>
<p>And goodness of fit is the error <script id="MathJax-Element-36" type="math/tex">\| \mathbf X \hat {\mathbf w} - \mathbf y \|</script>:</p>
<pre><code>def goodnessOfFit(drmX: DrmLike[Int], w: Vector, y: Vector) = {
val fittedY = (drmX %*% w) collect (::, 0)
(y - fittedY).norm(2)
}
</code></pre>
<p>Now we can call these functions:</p>
<pre><code>val sol = ols(drmX, y)
println(goodnessOfFit(drmX, sol, y))
</code></pre>
<p>And you’ll see the result:</p>
<pre><code>14.200396723606845
</code></pre>
<h1 id="million-song-dataset">Million Song Dataset</h1>
<p>Now when we got a bit familiar with the syntax, let’s try to analyze a bigger dataset. We will use “YearPredictionMSD” from <a href="http://archive.ics.uci.edu/ml/datasets/YearPredictionMSD">http://archive.ics.uci.edu/ml/datasets/YearPredictionMSD</a>. It’s the biggest dataset for regression at UCI ML repository and there are 515.000 instances. It’s a subset of the <a href="http://labrosa.ee.columbia.edu/millionsong/">Million Song Dataset</a>.</p>
<p>Here’s the description of the dataset: </p>
<blockquote>
<p>Abstract: Prediction of the release year of a song from audio features. Songs are mostly western, commercial tracks ranging from 1922 to 2011, with a peak in the year 2000s.</p>
<p>Attribute Information:</p>
<p>90 attributes, 12 = timbre average, 78 = timbre covariance <br>
The first value is the year (target), ranging from 1922 to 2011. <br>
Features extracted from the ‘timbre’ features from The Echo Nest API. <br>
We take the average and covariance over all ‘segments’, each segment being described by a 12-dimensional timbre vector.</p>
</blockquote>
<p>So in this dataset there are 90 different features extracted from songs, and the goal is to predict the year when the song was released. We can download it from <a href="http://archive.ics.uci.edu/ml/machine-learning-databases/00203/">http://archive.ics.uci.edu/ml/machine-learning-databases/00203/</a>.</p>
<p>The first step is setting up the environment the way we did before:</p>
<pre><code>val conf = new SparkConf()
.setAppName("Simple Application")
.setMaster("local[*]")
.set("spark.serializer",
"org.apache.spark.serializer.KryoSerializer")
.set("spark.kryo.registrator",
"org.apache.mahout.sparkbindings.io.MahoutKryoRegistrator")
implicit val sc = new SparkDistributedContext(new SparkContext(conf))
</code></pre>
<p>Let’s read the data. For that we’ll use Spark’s methods for reading from text files:</p>
<pre><code>val data = sc.textFile(textFilePath, numPartitions)
val rdd = data.map(line => line.split(",").map(_.toDouble)).map(dvec(_))
</code></pre>
<p>Now we have an RDD of <code>DenseVector</code> objects. DRM’s are indexed rows, so we want to add an index to each row with <code>withWithIndex</code> function in Spark. However, it would give us tuples <code>(Vector, Long)</code>, while we need <code>(Int, Vector)</code>, so we’ll need to do an additional step:</p>
<pre><code>val rddMatrixLike: DrmRdd[Int] =
rdd.zipWithIndex.map { case (v, idx) => (idx.toInt, v) }
</code></pre>
<p><code>DrmRdd[Int]</code> is an alias for <code>RDD[DrmTuple[Int]]</code>, and <code>DrmTuple[Int]</code> is an alias for <code>(Int, Vector)</code>, so <code>DrmRdd[Int]</code> is actually <code>DrmRdd[(Int, Vector)]</code>.</p>
<p>Now we convert the RDD to Mahout’s DRM: </p>
<pre><code>val matrix = drmWrap(rddMatrixLike)
</code></pre>
<p>In this data set there is a predefined training/test split, so let’s cut it: </p>
<pre><code>val nrow = matrix.nrow.toInt
val train = matrix(0 until 463715, ::)
val test = matrix(463715 until nrow, ::)
</code></pre>
<p>And extract the first column as target <script id="MathJax-Element-37" type="math/tex">\mathbf y</script>, and the rest as the data matrix <script id="MathJax-Element-38" type="math/tex">\mathbf X</script>:</p>
<pre><code>val ncol = matrix.ncol
val X = train(::, 1 until ncol)
val y = train.viewColumn(0)
</code></pre>
<p>Now using the <code>ols</code> procedure we already implemented, let’s run the regression:</p>
<pre><code>val w = ols(X, y)
println(w)
</code></pre>
<p>But we forgot to do an important preprocessing step: adding the bias term. So let’s write a function <code>addBias</code> that prepends <code>1</code> to every vector of our dataset</p>
<pre><code>def addBias(drmX: DrmLike[Int]) = {
drmX.mapBlock(drmX.ncol + 1) {
case (keys, block) => {
val block_new = block.like(block.nrow, block.ncol + 1)
block_new.zip(block).foreach {
case (row_new, row_orig) =>
row_new(0) = 1.0
row_new(1 to block.ncol) := row_orig
}
keys -> block_new
}
}
}
</code></pre>
<p>Let’s try to understand what’s happening here. We need to apply a transformation to every row of the dataset, and to do this we use the <code>mapBlock</code> function. It takes 2 arguments: the number of columns in the new DRM and a function to be applied to each block of the DRM. Recall that DRMs can be stored in row blocks, so each row block is itself a matrix (a object of class <code>Matrix</code>).</p>
<p>So the first thing is creating a new matrix that looks exactly like the old block, but with one additional column, by using the <code>like</code> method. It is possible to iterate over all rows of a <code>Matrix</code> because it implements <code>java.lang.Iterable</code> and it can be used in Java’s foreach loop. But to use them in Scala and to be able to use all Scala’s handy functions like <code>map</code>, we need to add the following imports: </p>
<pre><code>import collection._
import collection.JavaConversions._
</code></pre>
<p>This way it can wrap Java collections into Scala ones and enable us to use Scala functions on them. What we do here is <code>zip</code>ping two matrices - row-by-row, and then, for each pair, we assign the first element of the new row to 1.0 and copy the rest from the old row. </p>
<p>At the end we just return the pair of the input keys (untouched) and the block with rows with 1.0 prepended. </p>
<p>So now we can call it: </p>
<pre><code>val w = ols(addBias(X), y)
</code></pre>
<p>And calculate the goodness of fit on the testing set:</p>
<pre><code>val X_test = test(::, 1 until ncol)
val y_test = test.viewColumn(0)
val error = goodnessOfFit(addBias(X_test), w, y_test)
println(error)
</code></pre>
<p>The result is around 2160. </p>
<p>Ok, good. But can we do better? Maybe we are overfitting and need to regularize the model? Let’s try using the Ridge Regression on this dataset.</p>
<h2 id="regularization">Regularization</h2>
<p>Ridge Regression is a regularized version of OLS regression, and instead of solving <script id="MathJax-Element-39" type="math/tex">(\mathbf X^\mathbf T \mathbf X) \, \mathbf {\hat w} = \mathbf X^\mathbf T \mathbf y</script> we solve <script id="MathJax-Element-40" type="math/tex">(\mathbf X^\mathbf T \mathbf X + \mathbf I \lambda) \, \mathbf {\hat w} = \mathbf X^\mathbf T \mathbf y</script>.</p>
<p>So it’s pretty similar, but we need to add the regularization parameter <script id="MathJax-Element-41" type="math/tex">\lambda</script> to each diagonal element of <script id="MathJax-Element-42" type="math/tex">\mathbf X^\mathbf T \mathbf X</script>:</p>
<pre><code>def ridge(drmX: DrmLike[Int], y: Vector, lambda: Double = 1.0) = {
val XTX = drmX.t %*% drmX
val XTy = drmX.t %*% y
val reg = diag(lambda, XTX.ncol)
val sol = solve(XTX.plus(reg), XTy)
sol(::, 0)
}
</code></pre>
<p>Note that if we try to add a matrix to an DRM using the <code>plus</code> function, the DRM gets implicitly converted to a “in-core” matrix, so don’t do this for large matrices. </p>
<p>However for Ridge Regression we typically need to standardize the data, so every feature is on the same scale. Standardization is done by calculating <script id="MathJax-Element-43" type="math/tex">\tilde {\mathbf x} = \cfrac{\mathbf x - \boldsymbol \mu}{\boldsymbol \sigma}</script>. </p>
<p>Let’s write a function to do that:</p>
<pre><code>def standardize(drmX: DrmLike[Int])(implicit ctx: DistributedContext) = {
val meanVec = drmX.colMeans
val variance = (drmX * drmX).colMeans - (meanVec * meanVec)
val mean = drmBroadcast(meanVec)
val std = drmBroadcast(variance.sqrt)
val res = drmX.mapBlock(drmX.ncol) {
case (keys, block) => {
val copy = block.cloned
copy.foreach(row => row := (row - mean) / std)
(keys, copy)
}
}
res
}
</code></pre>
<p>To calculate means for all the features we use the <code>colMeans</code> function which returns a vector of means for each column. </p>
<p>To calculate variance, we use the formula <script id="MathJax-Element-44" type="math/tex">\text{var}(X) = \mathbb E \, [ X^2 ] - \big( \mathbb E \, [X] \big)^2</script> and the code for it is straightforward. </p>
<p>There’s one caveat though: by default, <code>drmX * drmX</code> (element-wise Matrix multiplication) has side effects. That is, executing <code>drmX * drmX</code> will change <code>drmX</code>, and it will write the results back to <code>drmX</code> as if we did <code>drmX := drmX * drmX</code>. I assume this is done for optimization reasons. Luckily there’s a way to change this behavior, and this is done by setting the <code>"mahout.math.AewB.inplace"</code> property to <code>false</code>: </p>
<pre><code>System.setProperty("mahout.math.AewB.inplace", "false")
</code></pre>
<p>You will need to add line in your header with the setup (or maybe in the configuration files in your <code>mahout/conf</code> folder).</p>
<p>As a side note: there’s an alternative method for computing the standard deviation without side effects: we first compute the covariance matrix <script id="MathJax-Element-45" type="math/tex">\mathbf C = \cfrac{1}{N - 1} \, (\mathbf X - \boldsymbol \mu) ^\mathbf T (\mathbf X - \boldsymbol \mu)</script> and then take elements on the diagonal of <script id="MathJax-Element-46" type="math/tex">\mathbf C</script>, but it is probably more expensive computationally. </p>
<p>This allows you to compute vectors <script id="MathJax-Element-47" type="math/tex">\boldsymbol \mu</script> and <script id="MathJax-Element-48" type="math/tex">\boldsymbol \sigma</script> with mean and standard deviation over the entire input space. </p>
<p>But the DRM we want to standardize lays across several machines, and what we want to do is to send the computed <script id="MathJax-Element-49" type="math/tex">\boldsymbol \mu</script> and <script id="MathJax-Element-50" type="math/tex">\boldsymbol \sigma</script> vectors to all these machines. This is done via the <code>drmBroadcast</code> method: it broadcasts the vectors and it’s possible to use them in the <code>mapBlock</code> function.</p>
<p>Now for each row <script id="MathJax-Element-51" type="math/tex">\mathbf x</script> we need to compute <script id="MathJax-Element-52" type="math/tex">\tilde {\mathbf x} = \cfrac{\mathbf x - \boldsymbol \mu}{\boldsymbol \sigma}</script>. We can do this transformation by using <code>mapBlock</code> function. Remember that DRMs could be stored in rows or in blocks of rows, and usually they are stored in blocks. This function applies a specified <code>map</code> function to every such block of your matrix (it will convert the matrix into the block representation if needed). </p>
<p>Next we need to apply this transformation to every row, and to do this we’ll use the familiar <code>mapBlock</code> function: </p>
<pre><code>val res = drmX.mapBlock(drmX.ncol) {
case (keys, block) => {
val copy = block.cloned
copy.foreach(row => row := (row - mean) / std)
(keys, copy)
}
}
</code></pre>
<p>Here we want to avoid side effects and therefore clone the matrix, and then we transform each row of the block matrix. </p>
<p>I think it looks more complex than it needs and I hope <a href="https://issues.apache.org/jira/browse/MAHOUT-1691">one day</a> there will be a simpler way of doing it, for example, something like this: </p>
<pre><code>val res = drmX.mapBlock(drmX.ncol) {
case (keys, block) => {
keys -> block.map(row => (row - mean) / std)
}
}
</code></pre>
<p>Another note: in many cases it is fine to have some side effects, if we’re not going to use our <code>drmX</code> again. So instead we can write </p>
<pre><code>val res = drmX.mapBlock(drmX.ncol) {
case (keys, block) => {
block.foreach(row => row := (row - mean) / std)
(keys, block)
}
}
</code></pre>
<p>Don’t forget that we need to do the standardization before adding the bias term, otherwise we will lose the intercept. Now let’s run it!</p>
<pre><code>val w_reg = ridge(addBias(standardize(X)), y, lambda = 10.0)
val error_reg = goodnessOfFit(addBias(standardize(X_test)), w_reg, y_test)
println("error ridge", error_reg)
</code></pre>
<p><script id="MathJax-Element-53" type="math/tex">\lambda = 10</script> is taken arbitrarily, and in real life we should use cross validation techniques for selecting it. Actually we see that the error doesn’t decrease at all, but at least we played around with some Mahout functionality. </p>
<p>You can try to implement <a href="http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29">Cross Validation</a> yourself: e.g. take another subset from <code>X_Train</code> and perform validation on it, or even split it in 10 subsets and do a 10-fold CV.</p>
<h3 id="dimensionality-reduction">Dimensionality Reduction</h3>
<p>90 attributes is quite a lot, so let’s try to reduce the dimensionality of our data set with using <a href="http://en.wikipedia.org/wiki/Principal_component_analysis">Principal Component Analysis</a> techniques and then run an OLS regression on it (this sometimes called “<a href="http://en.wikipedia.org/wiki/Principal_component_regression">Principal component regression</a>“). </p>
<p>Luckily there are algorithms in Mahout such as SVD and PCA that could be helpful in this. There are distributed implementations of these algorithms in the <br>
<code>org.apache.mahout.math.decompositions</code> package, for example, DSSVD (Distributed Stochastic SVD). Unfortunately, for this particular data set it took very long to compute SVD on my machine, so I stopped it after running 30 minutes. </p>
<p>But what we can do instead is to take a subsample of our training data and do in-memory SVD on this smaller matrix (which is also implemented in Mahout Math), and then learn the principal components and use this information to transform the entire training set as well as the testing set. </p>
<p>Let’s do it. First, we need to take a sample from the training set, and to do that we need to sample rows from the data matrix <script id="MathJax-Element-54" type="math/tex">\mathbf X</script>. Suppose we have an <script id="MathJax-Element-55" type="math/tex">N \times p</script> data matrix <script id="MathJax-Element-56" type="math/tex">\mathbf X</script> and we want to sample rows from this matrix. We can use linear algebra and matrix multiplication for it: we create a sampling matrix <script id="MathJax-Element-57" type="math/tex">\mathbf S</script> of size <script id="MathJax-Element-58" type="math/tex">M \times N</script> where <script id="MathJax-Element-59" type="math/tex">M</script> is the amount of rows you want to sample and <script id="MathJax-Element-60" type="math/tex">N</script> is the number of rows in the original matrix. We let element <script id="MathJax-Element-61" type="math/tex">S_{ij}</script> be 1 if we want to take row <script id="MathJax-Element-62" type="math/tex">j</script> from the original matrix <script id="MathJax-Element-63" type="math/tex">\mathbf X</script> and put it as a row <script id="MathJax-Element-64" type="math/tex">j</script> in the sampled matrix. Then, to get the rows, we multiply <script id="MathJax-Element-65" type="math/tex">\mathbf X</script> to <script id="MathJax-Element-66" type="math/tex">\mathbf S</script> on the right: <script id="MathJax-Element-67" type="math/tex">\mathbf S \mathbf X</script> will return the rows we need. </p>
<p>This matrix will mostly contain zero entries, i.e. it will be a very sparse matrix. Therefore, to save the memory, we will need to use sparse vectors to build the sampling matrix. Mahout <code>RandomAccessSparseVector</code> is a sparse implementation of the <code>Vector</code> interface. Let’s write the <code>sample</code> function:</p>
<pre><code>def sample(drm: DrmLike[Int], size: Int, seed: Int) = {
val nrow = drm.nrow.toInt
val rnd = new Random(seed)
val randomIdx = Seq.fill(size)(rnd.nextInt(nrow))
val Srows = randomIdx.map { j =>
val vec = new RandomAccessSparseVector(nrow)
vec.setQuick(j, 1)
vec
}
val S = new SparseRowMatrix(size, nrow)
S := Srows
S %*% drm
}
</code></pre>
<p>What we do here is setting up a random number generator and create a matrix <script id="MathJax-Element-68" type="math/tex">\mathbf S</script> where for each row <script id="MathJax-Element-69" type="math/tex">i</script> in <script id="MathJax-Element-70" type="math/tex">\mathbf S</script> we generate some random index <script id="MathJax-Element-71" type="math/tex">j</script> and set <script id="MathJax-Element-72" type="math/tex">S_{ij}</script> to 1. <code>S := Srows</code> is just a way of converting a sequence of vectors to a matrix: internally it assigns each row of the sequence to a row of the matrix. And finally we do a left multiplication to take rows from <script id="MathJax-Element-73" type="math/tex">\mathbf X</script>.</p>
<p>Now, after sampling some data points, we can do in-memory SVD:</p>
<pre><code>def dimRed(X_train: Matrix, dim: Int): DrmLike[Int] => DrmLike[Int] = {
val V = svd(X_train)._2
val V_red = V(::, 0 until dim)
def fit(X: DrmLike[Int]): DrmLike[Int] = {
X %*% V_red
}
fit
}
</code></pre>
<p>We apply SVD (it’s a way of doing PCA) to our matrix and take only first <code>dim</code> elements from the principal component matrix. This function returns another function that can be used to apply the learned transformation to other datasets, and it’s done simply by matrix multiplication. </p>
<p>Since we’re doing dimensional reduction via PCA, as we discussed earlier, it’s important to center all the features: transform them such that their expected value is 0. It’s really easy and very similar to what we did for <code>standardize</code>, but we only need to subtract the mean and don’t need to divide by standard deviation. </p>
<p>So we sample our matrix, then center it and train the PCA on the sample</p>
<pre><code>val X_subset = center(sample(X, 5000, seed = 0x31337))
val pca = dimRed(X_subset, dim = 20)
</code></pre>
<p>Value of 20 dimensions is taken arbitrarily here, in practice we usually keep enough dimensions to explain at least 95% of variance in the data set (for details you may refer, e.g. <a href="http://stats.stackexchange.com/questions/22569/pca-and-proportion-of-variance-explained">here</a>).</p>
<p>Now we apply the transformation we learned to the entire (recall that <code>dimRed</code> returns a function which we cann <code>pca</code> that is used for transformation) </p>
<pre><code>val w_pca = ols(addBias(pca(center(X))), y)
</code></pre>
<p>Let’s check how well it does:</p>
<pre><code>val error_pca = goodnessOfFit(addBias(pca(center(X_test))), w_pca, y_test)
println("error pca", error_pca)
</code></pre>
<p>We see that it prints 2390, so it’s worse than simple OLS regression. Maybe for this particular case we don’t keep enough dimensions, so you may try to experiment with selecting the right dimensionality for this data. </p>
<h1 id="conclusions">Conclusions</h1>
<p>Even though the simplest OLS regression for this data set in our experiments turned out to be the best method, we learned how to do many useful things with Mahout Samsara: how to distribute linear algebra operations and prepare the environment for this, how to incorporate it into Spark dataflows. We also learned a little bit about internals of Samsara: how matrices are represented and how we can apply efficient block-wise transformations to our matrices.</p>
<p>Mahout Samsara is a relatively new project and there are still many things that are not yet implemented, but needed. For example, in this post we had to implement calculating standard deviation and standardization ourselves. However it’s still a pretty promising tool. What’s more, it’s not just one implementation, but rather an API for performing distributed Linear Algebra calculations. Currently it’s running only on top of Apache Spark, but backends for other distributed engines are being implemented. For example for H2O and for Apache Flink. So you can take this library to your current data pipeline and have out-of-the-box distributed Linear Algebra library. </p>
<p>All the source code is available on <a href="https://github.com/alexeygrigorev/itshared-howto/tree/master/mahout-spark-binding">github</a>.</p>
<h2 id="acknowledgments">Acknowledgments</h2>
<p>This post is inspired by an article <a href="https://mahout.apache.org/users/sparkbindings/play-with-shell.html">“Playing with Mahout’s Spark Shell”</a> and I used the code and the data from there in the introduction. Also, the <a href="https://mahout.apache.org/users/sparkbindings/ScalaSparkBindings.pdf">Scala and Spark Bindings manual</a> gives a good overview of the syntax. The implementation of sampling rows from a matrix is taken from a paper “Efficient Sample Generation for Scalable Meta Learning” by Sebastian Schelter and others (<a href="http://ssc.io/wp-content/uploads/2014/11/ICDE15_research_150.pdf">pdf</a>). </p>
<p>Thanks for reading and stay tuned!</p>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com4tag:blogger.com,1999:blog-7332477090953898587.post-35488355454902726432015-03-09T09:00:00.000+01:002015-06-21T19:38:59.825+02:00Naive Bayes on Apache Flink<p><center><img border="0" src="http://habrastorage.org/files/b50/94b/39c/b5094b39c19a4d4d8ad532225e67a91f.png" /></center></p>
<p>In this blog post we are going to implement a Naive Bayes classifier in Apache Flink. We are going to use it for text classification by applying it to the 20 Newsgroup dataset. To understand what is going on, you should be familiar with Java and know what MapReduce is. If you have seen and understood a word count example in any system, you're good to go. If you haven't heard of MapReduce or haven't seen the word count, you may first have a look at our introductory post <a href="http://www.itshared.org/2015/03/hadoop-and-mapreduce.html">"Hadoop and MapReduce"</a>.</p>
<a name='more'></a>
<p>Here we will use:</p>
<ul>
<li>Java 7 (or 8)</li>
<li>Apache Maven</li>
<li>Apache Flink</li>
<li>Stanford NLP for text preprocessing</li>
</ul>
<p><a href="https://flink.apache.org/">Apache Flink</a> is a system for large scale data processing, like Apache Hadoop or Apache Spark. In this post we will use the Java API because it's simple to understand, although more verbose than the Scala API. The mapping to the Scala API should be straightforward, and if you want, you can implement the same thing in Spark with only minor modifications.</p>
<p>You don't need to install Apache Flink or build it from the sources, but you do need to have Apache Maven installed. You can download Maven from its <a href="http://maven.apache.org/download.cgi">official website</a>, but if you have latest Eclipse, IntelliJ Idea or NetBeans, they should already have it inside, so just make sure that you create a Maven project in your IDE. If you're not familiar with Maven, here's <a href="http://maven.apache.org/guides/getting-started/">a getting started guide</a>.</p>
<h1>Theory</h1>
<h2>Text Classification</h2>
<p><center><img src="http://habrastorage.org/files/707/5d4/556/7075d4556c23499eac561998e97160f7.png"></center></p>
<p>Let's quickly review the theory. In Machine Learning, classification is the problem of identifying to which class an observation belongs in. In text classification, the observations are documents, and the classes are document categories. So, based on some criteria, we need to assign a document to some category. A good example of text classification is deciding whether an email is spam or not. However, it doesn't have to be only two categories, and in this article we well try to assign documents to one out of 20 categories.</p>
<p>How do we represent a document? The usual way is to see a document as a collection of words: $$\text{doc} = ( w_1 , w_2 , \ ... \ , w_n )$$
For simplicity, we will assume that the order of words in the document and the grammatical relationships between the words are not important, and the only important thing is the words themselves and how many occurrences of each word is there. In Information Retrieval and NLP this representation is called the <a href="http://en.wikipedia.org/wiki/Bag-of-words_model">Bag-of-Words model</a>. This representation should be good enough for classification purposes: we may assume that some words tend to occur more frequently in one specific category than in others.</p>
<p>Next, for text classification, the Bayes Rule says that the probability that a document $\text{doc}$ belongs to the category $\text{cat}$ is given by
$$P(\text{cat} \mid \text{doc}) = \cfrac{P(\text{doc} \mid \text{cat}) \cdot P(\text{cat})}{P(\text{doc})}$$</p>
<p><center><a href="http://en.wikipedia.org/wiki/File:Thomas_Bayes.gif"><img src="http://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif"/></a></center><center><small>Thomas Bayes</small></center></p>
<h2>Naive Bayes</h2>
<p>The Naive Bayes assumption is that all the words in $\text{doc}$ are independent.
That is, if a document $\text{doc}$ is represented as $\text{doc} = ( w_1 , w_2 , \ ... \ , w_n )$, then the probability of seeing this document is $$P(\text{doc} \mid \text{cat}) = P( w_1 , w_2 , \ ... \ , w_n \mid \text{cat}) = \prod_i P(w_i \mid \text{cat})$$</p>
<p>To assign a document to some category, we select the category $\text{cat}^*$ with highest $P(\text{cat} \mid \text{doc})$: </p>
<p>$$\begin{align}
\text{cat}^* & = \operatorname{argmax}_\text{cat} \cfrac{P(\text{doc} \mid \text{cat}) \cdot P(\text{cat})}{P(\text{doc})} \\
& = \operatorname{argmax}_\text{cat} \cfrac{\prod P(w_i \mid \text{cat}) P(\text{cat})}{P(\text{doc})}
\end{align}$$</p>
<p>We can get rid of the denominator because it doesn't depend on $\text{cat}$:</p>
<p>$$\text{cat}^* = \operatorname{argmax}_\text{cat} \prod P(w_i \mid \text{cat}) P(\text{cat})$$</p>
<p>Note that we have to multiply lots of probability values, and many of them could be very small, and this may cause numerical underflow: when some numerical value is too small to be stored in memory. The typical solution to this problem is to take a log of this expression. Since logarithm is a monotonically increasing function, we get</p>
<p>$$\begin{align}
\text{cat}^* & = \operatorname{argmax}_\text{cat} \prod_i P(w_i \mid \text{cat}) P(\text{cat}) \\
& = \operatorname{argmax}_\text{cat} \log \left[ \prod_i P(w_i \mid \text{cat}) P(\text{cat}) \right] \\
& = \operatorname{argmax}_\text{cat} \left[ \sum_i \log P(w_i \mid \text{cat}) + \log P(\text{cat}) \right]
\end{align}$$</p>
<br/>
<h2>Estimating $P(\text{cat})$ and $P(w_i \mid \text{cat})$ </h2>
<p>So we have our formula:</p>
<p>$$\text{cat}^* = \operatorname{argmax}_\text{cat} \left[ \sum_i \log P(w_i \mid \text{cat}) + \log P(\text{cat}) \right]$$</p>
<p>We need two things here: $P(\text{cat})$ and $P(w_i \mid \text{cat})$. We estimate them using the data and obtain $\hat P(\text{cat})$ and $\hat P(w_i \mid \text{cat})$. </p>
<p>Estimation is very easy - it's just counting:</p>
<ul>
<li>$\hat P(\text{cat}) = \cfrac{\text{count(cat)}}{\text{total count}}$: we just count how many documents of this category are there</li>
<li>$\hat P(w_i \mid \text{cat}) = \cfrac{ \text{count($w_i$ in cat)} }{\sum_j \text{count($w_j$ in cat)}}$: in each category, we count how many times times a word $w_i$ occurs and divide it by the total number of words in the category</li>
</ul>
<p>So, with minor modification, we can reduce our calculations to the word count problem!</p>
<br/>
<h2>Smoothing</h2>
<p>What happens if we never see a certain word $w_0$ during the training, but it occurs during classification? In this case $\hat P(w_0 \mid \text{cat}) = 0$, and thus $\hat P(\text{doc} \mid \text{cat})$ is also zero: $P(\text{doc} \mid \text{cat}) = \prod\limits_i \hat P(w_i \mid \text{cat}) = 0$. We also would have problems in the log domain: $\hat P(w_0 \mid \text{cat}) = \log 0 = -\infty$, so $\log P(\text{doc} \mid \text{cat}) = \sum\limits_i \log \hat P(w_i \mid \text{cat}) = -\infty$.</p>
<p>How we can avoid that? Laplace Smoothing (or <a href="http://en.wikipedia.org/wiki/Additive_smoothing">Additive Smoothing</a>) is a simple and popular technique for this. We add some number $\lambda$ to each count: </p>
<p>$$\hat P(w_i \mid \text{cat}) = \cfrac{ \text{count($w_i$ in cat)} + \lambda }{ \sum_j \left(\text{count($w_j$ in cat)} + \lambda \right)}$$</p>
<p>Thus, unseen words will have estimated probability $$\hat P(w_0 \mid \text{cat}) = \cfrac{ \lambda }{\sum_j \left( \text{count($w_j$ in cat)} + \lambda \right)} \ne 0$$.</p>
<p>We can further rewrite this as </p>
<p>$$\hat P(w_i \mid \text{cat}) = \cfrac{ \text{count($w_i$ in cat)} + \lambda }{\sum_j \text{count($w_j$ in cat)} + \sum_j \lambda }$$</p>
<p>So we can move the $\lambda$ out of this sum and then recognize that in $\sum_j \lambda$ the index $j$ goes over all seen words, so $\sum_j \lambda = \lambda \cdot \text{# distinct words}$:</p>
<p>$$\hat P(w_i \mid \text{cat}) = \cfrac{ \text{count($w_i$ in cat)} + \lambda }{\sum_j \text{count($w_j$ in cat)} + \lambda \cdot \text{# distinct words}}$$</p>
<p>Different values of the smoothing parameter $\lambda$ will lead to different probability estimates, and hence, by varying it, we can obtain different classification results. A very common value is $\lambda = 1$, and it's called "$+1$ smoothing".</p>
<p>This is enough to start coding.</p>
<h1>Coding</h1>
<h2>Data set</h2>
<p>There are quite a lot text of data sets for text classification on the Internet. A good collection of them could be found here: <a href="http://disi.unitn.it/moschitti/corpora.htm">http://disi.unitn.it/moschitti/corpora.htm</a>.</p>
<p>We are going to use one of them: the <a href="http://qwone.com/~jason/20Newsgroups/">20 Newsgroup dataset</a>. Here's the description: </p>
<blockquote>
<p>it contains 19997 articles for 20 categories taken from the Usenet newsgroups collection. We used the subject and the body of each message only. Some of the newsgroups are very closely related to each other (e.g., IBM computer system hardware / Macintosh computer system hardware), while others are highly unrelated (e.g. misc forsale / social religion and christian). This corpus is different from the previous corpora because it includes a larger vocabulary and words typically have more meanings. Moreover, the stylistic writing (e-mail dialogues) is very distant from the other more technical collections.</p>
</blockquote>
<p>We are going to use the one with training/test split, so let's download it:</p>
<pre><code>wget http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz
tar -xzf 20news-bydate.tar.gz
</pre></code>
<p>If you want, you may skip over the preprocessing steps (discussed below) and download already processed data from <a href="https://github.com/alexeygrigorev/datasets/tree/master/20-newsgroups/preprocessed">here</a>.</p>
<h2>Initial Preprocessing</h2>
<p>Let's have a look at how the files look like:</p>
<blockquote>
<pre>From: ho@cs.arizona.edu (Hilarie Orman)
Subject: Re: Licensing of public key implementations
Organization: U of Arizona, CS Dept, Tucson
Lines: 6
With regard to your speculations on NSA involvement in the creation
of PKP, I find that it fails the test of Occam's butcher knife. Never
attribute to conspiracy what can be explained by forthright greed.
Hilarie Orman
</pre>
</blockquote>
<p>This file above is from the "sci.crypt" category and has id=14989. So we see that it's indeed a big collections of email-like files, each of which is stored in a folder with category name. Such layout is very uncommon in Big Data, because there are lots of very small files, and it's not very effective to store them on a distributed file system. So we first need to convert this set of small files into a big one.</p>
<p>To do this we can create a small python script which will discard the header, remove all the linebreaks and then write the entire content as a tuple <code>(category, content)</code> in a bigger file. The script is very simple, but if you want, you can have a look at it <a href="https://github.com/alexeygrigorev/itshared-howto/blob/master/naive-bayes/scripts/preprocessing.py">here</a>. The results of this step can be found <a href="https://github.com/alexeygrigorev/datasets/tree/master/20-newsgroups/raw">here</a>.</p>
<br/>
<h2>Further Preprocessing</h2>
<p>Now we need to work with the text data more, and it will involve the following steps: </p>
<ul>
<li>tokenization: split a text into separate tokens, e.g. "I love cookies!" to ["I", "love", "cookies", "!"]</li>
<li>lemmatization: reduce all forms of the same word to the common one, e.g. "walk", "walking", "walked" all get reduced to "walk"</li>
<li>stop words removal: discard all words that carry no meaning but occur very commonly, e.g. "a", "the", "and", etc.</li>
</ul>
<p>To do this we will use <a href="http://nlp.stanford.edu/software/corenlp.shtml">Stanford NLP</a>, a library for natural language processing for Java. </p>
<p>First, let's add the library to our pom:</p>
<pre><code><dependency>
<groupId>edu.stanford.nlp</groupId>
<artifactId>stanford-corenlp</artifactId>
<version>3.5.1</version>
</dependency>
<dependency>
<groupId>edu.stanford.nlp</groupId>
<artifactId>stanford-corenlp</artifactId>
<version>3.5.1</version>
<classifier>models</classifier>
</dependency>
</pre></code>
<p>We also will need <a href="http://commons.apache.org/proper/commons-lang/">Apache Commons Lang 3</a>:</p>
<pre><code><dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>3.3.2</version>
</dependency>
</pre></code>
<p>And now we can use Stanford NLP to create a pipeline that will tokenize the text and return the lemmas:</p>
<pre><code>// create
Properties props = new Properties();
props.put("annotators", "tokenize, ssplit, pos, lemma");
StanfordCoreNLP pipeline = new StanfordCoreNLP(props);
// use
Annotation document = new Annotation(body);
pipeline.annotate(document);
List<CoreLabel> tokenized = document.get(TokensAnnotation.class);
for (CoreLabel token : tokenized) {
String lemma = token.get(LemmaAnnotation.class);
// process lemma
}
</pre></code>
<p>(You may notice that we have <code>ssplit</code> and <code>pos</code>: they are necessary for lemmatization, and therefore they are also included to the pipeline.)</p>
<p>In our case, we also need to remove stop words, punctuation marks, emails and other things with no words in them. So we can use this simple filter before returning a word to the user:</p>
<pre><code>private boolean valid(String lemma) {
if (lemma.length() < 2) {
return false;
}
if (stopwords.contains(lemma)) {
return false;
}
return StringUtils.isAlpha(lemma);
}
</pre></code>
<p>The last line uses a method of the <code>StringUtils</code> class from Commons Lang that checks whether a string contains only letters or also something else (digits, special characters, etc). </p>
<p>Lastly, in the pipeline we use a Part-of-Speech tagger before the lemmatization, and "bad" symbols like "^", "~", "#" tend to confuse the tagger, so it also makes sense to remove them before parsing. </p>
<p>The whole class for NLP preprocessing is <a href="https://github.com/alexeygrigorev/itshared-howto/blob/master/naive-bayes/src/main/java/org/itshared/flink/naivebayes/NlpPreprocessor.java">here</a>.</p>
<p>Now it's time to use Flink: we can use it to apply the NLP preprocessing to each document.</p>
<p>Let's start with adding dependencies to Flink:</p>
<pre><code><dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients</artifactId>
<version>0.8.1</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>0.8.1</version>
<scope>provided</scope>
</dependency>
</pre></code>
<p>At the moment of writing, the last version is <code>0.8.1</code>, but you may want to check if there's a newer version and use it. You can check this in <a href="http://mvnrepository.com/artifact/org.apache.flink">Flink's repository at maven central</a>.</p>
<p>If you have a problem like "missing artifact jdk.tools", then there's a hack to make it go away: you can exclude the dependency on <code>jdk.tools</code> from the flink dependency. See the whole <a href="https://github.com/alexeygrigorev/itshared-howto/blob/master/naive-bayes/pom.xml">pom.xml</a> for details.</p>
<p>Let's set up a Flink job. For that, create a Java class with the <code>main</code> method and write the following:</p>
<pre><code>ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
DataSource<String> input = env.readTextFile(inputPath);
DataSet<Tuple2<String, String>> output = input.flatMap(new NlpProcessingMapper());
output.writeAsCsv(outputPath, "\n", "\t", WriteMode.OVERWRITE);
</code></pre>
<p><code>ExecutionEnvironment</code> is the class that keeps all the information about the execution of Flink's jobs. If you just run this job locally, it will be executed in the local environment, but if you submit it to the cluster, it will use a different environment, but the user doesn't need to change anything in the code.</p>
<p>The environment knows how to read files from a specified location. In our case, the location is stored in the <code>inputPath</code> variable: this is the path to the data source we want to process. Here we read a tab-separated text file, and then apply a <code>flatMap</code> function to each document (that is, to each line of the input file). FlatMap is similar to the <code>map</code> function, but for each input element it can produce 0 or more output elements. What we want to do here is to take a document, process it, and output a tuple <code>(category, comma-separated-words)</code> for valid documents, or no tuple at all if the input is invalid for some reason. The <code>flatMap</code> function takes an implementation of <code>FlatMapFunction</code> as its argument:</p>
<pre><code>public static class NlpProcessingMapper extends
RichFlatMapFunction<String, Tuple2<String, String>> {
private NlpPreprocessor processor;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
processor = NlpPreprocessor.create();
}
@Override
public void flatMap(String value, Collector<Tuple2<String, String>> out)
throws Exception {
String[] split = value.split("\t");
if (split.length < 3) {
return;
}
String category = split[0];
List<String> words = processor.processBody(split[2]);
if (!words.isEmpty()) {
out.collect(new Tuple2<>(category, StringUtils.join(words, ",")));
}
}
}
</pre></code>
<p>Note that <code>NlpProcessingMapper</code> doesn't implement the <code>FlatMapFunction</code> interface directly, but instead it extends <code>RichFlatMapFunction</code>, which is an abstract implementation of <code>FlatMapFunction</code>. The reason is that we want to initialize the <code>NlpPreprocessor</code> before processing the data, and this could be done by overriding the <code>open</code> method. This function will be called before the mapper is used on the data, so <code>NlpPreprocessor</code> will be already initialized when we use it inside the <code>flatMap</code> function.</p>
<p>We can run it locally (just press run in your IDE) or on a server. In this post we will run only locally, but it's quite easy to run it on a stand-alone Flink instance, because we don't need to make any changes to the code.</p>
<p>And a final note: if you want speed and don't care much about the form of the words you end up using, you can use stemming intead of lemmatization: it should increase the processing speed because there will be no need to do POS tagging and other expensive things. You can read more on the difference between lemmatization and stemming <a href="http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html">here</a>. For example you can use a stemmer from <a href="http://lucene.apache.org/core/">Apache Lucene</a>.</p>
<h2>Training: Probability Estimation</h2>
<p>We are finally ready to move on to the training phase. </p>
<p>Recall the formula:</p>
<p>$$\text{cat}^* = \operatorname{argmax}_\text{cat} \left[ \sum_i \log P(w_i \mid \text{cat}) + \log P(\text{cat}) \right]$$</p>
<p>We reduced the problem to counting words:</p>
<p>$$\hat P(w_i \mid \text{cat}) = \cfrac{ \text{count($w_i$ in cat)} + \lambda }{\sum_j \text{count($w_j$ in cat)} + \lambda \cdot \text{# distinct words}}$$</p>
<p>We have three things to count here:</p>
<ol>
<li>$\text{count(} w_i \text{ in cat)}$: how many times a word $w_i$ is seen in category $\text{cat}$,</li>
<li>$\sum_j \text{count(} w_j \text{ in cat)}$: the total number word occurrences in category $\text{cat}$,</li>
<li>$\text{# distinct words}$: the total number of distinct words in the corpus.</li>
</ol>
<p>Now, when we know what we're going to calculate, let's read the training data:</p>
<pre><code>ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
DataSource<String> input = env.readTextFile(Config.TRAIN_DATA);
</pre></code>
<p>The first thing is $\text{count(} w_i \text{ in cat)}$: how many times a word $w_i$ is seen in category $\text{cat}$. This is calculated by counting the documents in each category and then by dividing each count by the total number of documents:</p>
<pre><code>DataSet<Tuple2<String, Long>> labelFrequencies =
input.map(new LabelExtraction()).groupBy(0).sum(1);
public static class LabelExtraction implements
MapFunction<String, Tuple2<String, Long>> {
@Override
public Tuple2<String, Long> map(String value) throws Exception {
return new Tuple2<>(value.split("\t")[0], 1L);
}
}
</pre></code>
<p>It's very similar to the word count problem: for each document we output only its label along with <code>1</code>, so we have tuples <code>(category, 1)</code>, and then we group by the category and sum over all these ones to get the total number of elements in the category.</p>
<p>Next, we need to normalize the counts so they sum up to one, and for this we find the total sum. To do it, we just sum over <code>labelFrequencies</code> again:</p>
<pre><code>DataSet<Tuple1<Long>> totalSum = labelFrequencies.sum(1).project(1);
</pre></code>
<p>The sum over <code>(category, count)</code> also produces a tuple with something on the first position and the total count on the second. We are interested only in the count, so we take a projection on the second element (it starts counting from 0).</p>
<p>Now we need to divide all elements of <code>labelFrequencies</code> by the total sum. It's a bit trickier: once we obtained the total count, we need to send this value to all the mappers across the cluster. This is done by "broadcasting" the variable:</p>
<pre><code>DataSet<Tuple2<String, Double>> priors =
labelFrequencies.map(new NormalizationMapper())
.withBroadcastSet(totalSum, "totalSum");
public static class NormalizationMapper extends
RichMapFunction<Tuple2<String, Long>, Tuple2<String, Double>> {
private long totalSum;
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
List<Tuple1<Long>> totalSumList =
getRuntimeContext().getBroadcastVariable("totalSum");
this.totalSum = totalSumList.get(0).f0;
}
@Override
public Tuple2<String, Double> map(Tuple2<String, Long> value)
throws Exception {
return new Tuple2<>(value.f0, ((double) value.f1) / totalSum);
}
}
</pre></code>
<p>Note <code>withBroadcastSet(totalSum, "totalSum")</code>: this is how the value is broadcasted to all the mappers. We again use a rich function <code>RichMapFunction</code>, and it allows us to access the runtime context and get the variable.</p>
<p>So we run this and get the following values:</p>
<table>
<tr><td>alt.atheism</td> <td>0.042</td></tr>
<tr><td>comp.sys.ibm.pc.hardware</td> <td>0.051</td></tr>
<tr><td>comp.windows.x</td> <td>0.052</td></tr>
<tr><td>misc.forsale</td> <td>0.051</td></tr>
<tr><td>rec.autos</td> <td>0.052</td></tr>
<tr><td>rec.motorcycles</td> <td>0.052</td></tr>
<tr><td>rec.sport.baseball</td> <td>0.052</td></tr>
<tr><td>rec.sport.hockey</td> <td>0.053</td></tr>
<tr><td>...</td> <td>...</td></tr>
</table>
<p>We see that actually all the priors are almost the same for all the classes, so we might just as well skip this computation, but let's keep them anyway for a more general case. </p>
<p>Next, we need to compute the word count per each category. To do this, first, for each word of a document we omit a tuple <code>(category, word, 1)</code>, and then after grouping by <code>(category, word)</code> we sum over the last column:</p>
<pre><code>DataSet<Tuple3<String, String, Integer>> labelledWords =
input.flatMap(new TokenReaderMapper());
DataSet<Tuple3<String, String, Integer>> wordCount =
labelledWords.groupBy(1, 0).sum(2);
</pre></code>
<p><code>TokenReaderMapper</code> here is very simple: it splits the input by <code>\t</code> (tabulation) and then for the second part of the split, it splits further by <code>,</code>, and finally outputs a tuple <code>(category, word, 1)</code> for each word:</p>
<pre><code>public static class TokenReaderMapper implements
FlatMapFunction<String, Tuple3<String, String, Integer>> {
@Override
public void flatMap(String inputValue,
Collector<Tuple3<String, String, Integer>> out)
throws Exception {
String[] split = inputValue.split("\t");
String category = split[0];
for (String word : split[1].split(",")) {
out.collect(new Tuple3<>(category, word, 1));
}
}
}
</pre></code>
<p>As the result it will produce tuples like these:</p>
<table>
<tr><td>alt.atheism</td><td>agnosticism</td><td>12</td></tr>
<tr><td>comp.windows.x</td><td>dump</td><td>25</td></tr>
<tr><td>rec.autos</td><td>broken</td><td>2</td></tr>
</table>
<p>Finally, for we need to know how many words are there per category, so we count it this way:</p>
<pre><code>DataSet<Tuple2<String, Integer>> countPerCategory =
labelledWords.groupBy(0).sum(2).project(0, 2);
</pre></code>
<p>Since we have tuples <code>(category, word, 1)</code>, we just group by category, sum over the columns of ones and finally keep only <code>(category, count)</code> tuples.</p>
<p>The result is something like this:</p>
<table>
<tr><td>alt.atheism</td> <td>72270</td></tr>
<tr><td>comp.sys.ibm.pc.hardware</td> <td>51321</td></tr>
<tr><td>comp.windows.x</td> <td>82099</td></tr>
<tr><td>...</td> <td>...</td></tr>
</table>
<p>You can find the code for the entire class <code>WordCounterJob</code> <a href="https://github.com/alexeygrigorev/itshared-howto/blob/master/naive-bayes/src/main/java/org/itshared/flink/naivebayes/WordCounterJob.java">here</a>. </p>
<p>You may wonder why we created two separate jobs: one for NLP preprocessing and one for counting. There's no particular reason and in the real life you probably may want to do it in one job. But on the other hand splitting it into two parts allows us to store the intermediate results and it's also easier for debugging. </p>
<p>Now we're finally ready for classification.</p>
<h2>Classification</h2>
<p>Here we will assume that all the counts will fit into memory: all the heavy lifting (i.e. counting) has been done already at the previous step, so we just use the results. For very large datasets it will not be the case, so we would need do some joins before classification. Alternatively we could use approximation techniques like <a href="http://en.wikipedia.org/wiki/Count%E2%80%93min_sketch">Count-min Sketch algorithm</a> for getting counts of words for each category.</p>
<p>In the classification part for each element of the test set we try to predict the label using the Naive Bayes classifier using the probability estimates from the previous step.</p>
<p>First we need to read things we computed on the previous step:</p>
<pre><code>DataSet<Tuple2<String, Double>> priors =
env.readTextFile(Config.OUT_PRIOR).map(new PriorsReaderMapper());
DataSet<Tuple2<String, Integer>> totalCountPerCategory =
env.readTextFile(Config.OUT_TOTAL_COUNT_PER_CAT)
.map(new TotalCountPerCategoryMapper());
DataSet<Tuple3<String, String, Integer>> counts =
env.readTextFile(Config.OUT_COND_COUNT)
.map(new CountsReaderMapper());
</pre></code>
<p>All mappers here just convert the text input to tuples.</p>
<p>Each test instance will be classified in a map function <code>ClassifierMapper</code>, and this function will output a tuple <code>(actualClass, predictedClass)</code> for each document in the testing set. But first we need to transmit all the calculated data to the mappers, and we do it by broadcasting the corresponding data sets:</p>
<pre><code>int smoothing = 1;
testSet.map(new ClassifierMapper(smoothing))
.withBroadcastSet(priors, "priors")
.withBroadcastSet(counts, "counts")
.withBroadcastSet(totalCount, "totalCount");
</pre></code>
<p>The ClassifierMapper is a <code>RichMapFunction</code>:</p>
<pre><code>public static class ClassifierMapper
extends RichMapFunction<String, Tuple2<String, String>> {
private int smooting;
private NaiveBayesClassifier classifier;
public ClassifierMapper(int smooting) {
this.smooting = smooting;
}
@Override
public void open(Configuration parameters) throws Exception {
super.open(parameters);
RuntimeContext ctx = getRuntimeContext();
List<Tuple2<String, Integer>> countPerCategory =
ctx.getBroadcastVariable("countPerCategory");
List<Tuple2<String, Double>> priors =
ctx.getBroadcastVariable("priors");
List<Tuple3<String, String, Integer>> counts =
ctx.getBroadcastVariable("counts");
classifier = new NaiveBayesClassifier(smooting);
classifier.init(priors, counts, countPerCategory);
}
@Override
public Tuple2<String, String> map(String value) throws Exception {
String[] split = value.split("\t");
String actualLabel = split[0];
String[] words = split[1].split(",");
String predictedLabel = classifier.predict(words);
return new Tuple2<>(actualLabel, predictedLabel);
}
}
</pre></code>
<p>As previously, we access the broadcasted variables in the <code>open</code> method via the runtime context, and then we use them to initialize the classifier.</p>
<p>So the only remaining part here is the classifier itself. </p>
<p>Inside, it will have the following:</p>
<ul>
<li>prior probabilities: a <code>Map<String, Double></code> from category to double</li>
<li>count per category: a <code>Map<String, Integer></code> from category to count</li>
<li>conditional word counts: a <code>Table<String, String, Integer></code> from <code>(category, word)</code> to count</li>
<li>list of all labels <code>List<String></code></li>
</ul>
<p>All these fields are initialized when we call <code>classifier.init(priors, counts, countPerCategory);</code></p>
<p>A <code>Table</code> here is a class from Google Guava, and in essence it's a map of a map: it's roughly equivalent to <code>Map<String, Map<String, Integer>></code> but has some additional functionality. To use Guava, you need to add this dependency to your pom:</p>
<pre><code><dependency>
<groupId>com.google.guava</groupId>
<artifactId>guava</artifactId>
<version>18.0</version>
</dependency>
</pre></code>
<p>Finding the class with highest probability is trivial: we just try all possible classes and take the one with highest (log) probability.</p>
<pre><code>double maxLog = Double.NEGATIVE_INFINITY;
String predictionLabel = "";
for (String label : labels) {
double logProb = calculateLogP(label, words);
if (logProb > maxLog) {
maxLog = logProb;
predictionLabel = label;
}
}
</pre></code>
<p>And finally, the log of the probability for each word is computed by </p>
<p>$$\text{cat}^* = \operatorname{argmax}_\text{cat} \left[ \sum_i \log P(w_i \mid \text{cat}) + \log P(\text{cat}) \right]$$</p>
<p>where $\hat P(w_i \mid \text{cat})$ is</p>
<p>$$\hat P(w_i \mid \text{cat}) = \cfrac{ \text{count($w_i$ in cat)} + \lambda }{\sum_j \text{count($w_j$ in cat)} + \lambda \cdot \text{# distinct words}}$$</p>
<p>Which in code looks like this:</p>
<pre><code>double logProb = priors.get(label);
Map<String, Integer> countsPerLabel = conditionalWordCounts.row(label);
// numerator terms
for (String word : words) {
Integer count = countsPerLabel.get(word);
if (count != null) {
logProb = logProb + Math.log(count + smoothing);
} else {
logProb = logProb + Math.log(smoothing);
}
}
// denominator terms
double denom = countPerCategory.get(label) + smoothing * distinctWordCount;
logProb = logProb - words.length * Math.log(denom);
</pre></code>
<p>In the numerator, we sum over all word counts. Note that if a word is not present (it happens if we didn't see this word in category <code>label</code> during training), we add only the smoothing parameter $\lambda$. For each word the denominator is the same, so at the end we just subtract <code>denom</code> times number of words in the document. </p>
<h2>Accuracy</h2>
<p>There is one final thing: how do we check the accuracy of our model? From the previous step we have tuples <code>(actual_label, predicted_label)</code>. </p>
<p>There's a simple way to calculate the accuracy from these tuples:</p>
<ul>
<li>for each tuple omit <code>(1, 1)</code> if <code>actual_label == predicted_label</code> and <code>(0, 1)</code> otherwise</li>
<li>now reduce these tuples by summing element-wise: for two tuples <code>(l1, r1)</code> and <code>(l2, r2)</code> produce one tuple <code>(l1 + l2, r1 + r2)</code></li>
<li>after reducing in the first position we have the number of correctly classified documents and the total number of documents in the second</li>
<li>finally, divide first by second to get the final accuracy</li>
</ul>
<pre><code>predictions.map(new MatchMapper())
.reduce(new PerformaceEvaluatorReducer())
.map(new PerformaceMapper())
</pre></code>
<p>So everything is ready and we can fire the execution. After completing it reports the accuracy 0.74. Not perfect, but also not very bad, especially considering the nature of the data. </p>
<p>The full code for this class is <a href="https://github.com/alexeygrigorev/itshared-howto/blob/master/naive-bayes/src/main/java/org/itshared/flink/naivebayes/EvaluationJob.java">here</a>. </p>
<h1>Conclusions</h1>
<p>In this post we implemented the Naive Bayes algorithm for text classification. We set an NLP pipeline and then did some simple counting in Apache Flink. Of course, the dataset that we used is quite small and it would be much faster to do the same computation in Python or Java with no tools for parallel computation. Also, the accuracy of 75% is not perfect and there is still a lot of room for improvement. For example, we could do additional preprocessing and be more careful with the way we do NLP: not just take the ready-to-use pipeline, but experiment with different settings and see which one produces the best results. We also could experiment more with the smoothing parameter $\lambda$: maybe $\lambda = 1$ is not the best parameter and we can do better.</p>
<p>However, the goal was to implement Naive Bayes in Flink and we successfully accomplished it. We also saw that computations for this classifier are easy to parallelize because it involves only counting, so this classifier potentially can handle very large data sets.</p>
<p>The entire project is accessible at <a href="https://github.com/alexeygrigorev/itshared-howto/tree/master/naive-bayes">github</a>.</p>
<p>If you would like to learn more about Apache Flink, start with the official documentation: the docs for the current stable version (0.8) are available at <a href="https://flink.apache.org/docs/0.8/">https://flink.apache.org/docs/0.8/</a>. You may also want to follow <a href="http://data-artisans.com/blog.html">dataArtisans's blog</a> - they are the company behind Apache Flink.</p>
<h1>Acknowledgments</h1>
<p>This post is based on materials presented in the "Scalable Data Mining" course by Sebastian Schelter, Christoph Boden and Juan Soto from TU Berlin, and I took this class as a part of the IT4BI curriculum in the winter semester of 2014-2015.</p>
<p>You can read more about classes we took at TU Berlin <a href="http://www.itshared.org/2015/01/it4bi-distributed-and-large-scale.html">here</a>, and if you are interested, you can check other posts tagged with <a href="http://www.itshared.org/search/label/IT4BI">IT4BI</a> on this website.</p>
<p>Thanks for reading this post and stay tuned!</p>
<p><center><img src="http://habrastorage.org/files/a8b/0e3/5e3/a8b0e35e3862431ea6e6606b95ccdfdd.jpg"/></center></p>
Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com2tag:blogger.com,1999:blog-7332477090953898587.post-43627400008325141732015-03-04T21:06:00.000+01:002015-06-19T10:55:40.462+02:00Hadoop and MapReduce<p><center><img src="http://wikibon.org/w/images/c/cb/Mapreduce.jpg" /></center></p>
<br/>
<p>In this article we will briefly discuss the computation paradigm MapReduce,
and Apache Hadoop as one of its implementations. We won't get into much details, and we
even won't implement the Word Count on Hadoop, but it should give some foundation
for the future articles about tools for scalable data processing.</p>
<a name='more'></a>
<h1>MapReduce</h1>
<p><center><img src="http://habrastorage.org/files/fbc/33e/2b2/fbc33e2b26894055b3a2c8d5d272aebb.jpg" /></center>
<center>Source: <a href="https://twitter.com/tgrall">@tgrall</a></center></p>
<p><em>MapReduce</em> is a paradigm for parallel computation that comes
from Functional Programming, it's mainly used for large scale data processing and it typically runs on distributed systems.
It can hide the details of parallel execution, yet it's expressive enough for many data processing algorithms. </p>
<p>In MapReduce the two main primitives are functions <strong>map</strong> and
<strong>reduce</strong>, and both of them must be provided by a programmer, the rest is done
by the executing engine. </p>
<p>A <strong>map</strong> function takes a key-value pair (<code>in_key</code>, <code>in_value</code>) and produces
multiple key-value pairs called “intermediate result”.
The output of the map function is then grouped by keys and fed to the <strong>reduce</strong> function, which typically aggregates its input and produces the final result.</p>
<p>A <em>MapReduce job</em> is an execution of these two function on some data
set. A job consists of three stages:</p>
<ol>
<li>
<strong>the map stage</strong>, where the map function is applied to each
key-value pair of the input dataset
</li>
<li>
the grouping stages, at which the intermediate results are shuffled
and combined, and
</li>
<li>
finally <strong>the reduce stage</strong>, where the reduce function is applied
to each group of the intermediate result and produces the final
result.
</li>
</ol>
<p>A job is executed in parallel with many nodes reading from many input
sources.</p>
<p><center><img src="http://habrastorage.org/files/9f4/126/a0c/9f4126a0c41c47c7a21c3f4888f5966e.png" alt="A MapReduce job: (1) map phase, (2) grouping, (3) reduce
phase" /></center></p>
<p>Let's have a look at a classical example: the word count problem. Given some text, we need to calculate how many occurrences of each word there are. </p>
<p>In a python-like pseudo-code the map function is defined like this:</p>
<pre><code>def map(String input_key, String doc):
for each word w in doc:
EmitIntermediate(w, 1)
</code></pre>
<p>Here at the map stage, for each each input word $w$ the mapper outputs a tuple $(w, 1)$ by using <code>EmitIntermediate(w, 1)</code>.</p>
<p>After this, we group all produced tuples by key. That is, if we have $N$ words $w_1, \ ... \ , w_N$, then it the grouping for each word $w_i$ will be $(w_i, [1, 1, ..., 1])$: a word $w_i$ followed by a sequence of ones.</p>
<p>Finally, we define the reduce function:</p>
<pre><code>def reduce(String output_key, Iterator output_vals):
int res = 0
for each v in output_vals:
res = res + v
Emit(res)
</code></pre>
<p>Here we just sum over all the ones in each group and that will be the number of occurrences for each word. </p>
<p>Now let's try to make sense of the sandwich picture from the beginning: for the map phase, each input vegetable (or a loaf) is cut and the output could be <code>(sandwich_id, piece)</code>. Then at the reduce phase every tuple is grouped by the key, in our case <code>sandwich_id</code>, so all the needed ingredients are put together, and the reducer can assemble the sandwich. </p>
<h1>Apache Hadoop</h1>
<p><center><img src="http://hadoop.apache.org/images/hadoop-logo.jpg" /></center></p>
<p><a href="http://hadoop.apache.org/">Apache Hadoop</a> is an open-source implementation of the MapReduce. </p>
<p>However it's not always clear what the term “Hadoop” means, and people may refer to different things when they use it. Most commonly, it's used when talking about the MapReduce component and the Hadoop Distributed File System (HDFS). Also the term sometimes may refer to
other Hadoop-related products such as Apache HBase, Apache Hive, Apache Mahout and many others.</p>
<h2>Hadoop MapReduce Component</h2>
<p>Hadoop MapReduce Component is a data processing tool that works on top
of HDFS and implements MapReduce. </p>
<p>A Hadoop cluster consists of the main node, called <em>Name Node</em> that
orchestrates the process of assigning jobs and monitoring the progress.
A MapReduce job is done by data nodes, also called <em>workers</em>. There are
two kind of workers: <em>mapper</em> workers and <em>reduce</em> workers.</p>
<p>This picture (from an article by the <a href="http://escience.washington.edu/get-help-now/what-hadoop">University of Washington</a>) is an excellent way of describing how Hadoop works:</p>
<p><img src="http://habrastorage.org/files/510/049/78d/51004978da3342409a334476e2274594.png" alt="A MapReduce framework on Hadoop" /></p>
<ul>
<li>The Name Node chooses some idle workers and assigns them a map task</li>
<li>
Before starting the map task, the workers need to do some
preparation work:
<ul>
<li>
The specified input file is loaded to the file system and
partitioned into blocks
</li>
<li>
Then each block is replicated three times to guarantee
fault-tolerance
</li>
</ul>
</li>
<li>
The <strong>map phase</strong>
<ul>
<li>Each block of data is assigned to a map worker</li>
<li>The worker applies the <strong>map function</strong> to it</li>
<li>
Once the map function finishes its work, the intermediate
results are sorted on local disk of mapper
</li>
<li>
Also the intermediate results there are partitioned into $R$
groups (partitioning is typically done by hashing) - these
groups will be used at the reduce phase
</li>
</ul>
</li>
<li>It needs to wait until <em>all</em> map tasks are completed</li>
<li>
Before the reduce phase starts
<ul>
<li>The Name Node assigns reduce task to idle workers (reducers)</li>
<li>The intermediate results are shuffled and assigned to reducers</li>
<li>
Each reducer pulls its partition from the mapper’s disk (all map
results are already partitioned by mappers)
</li>
</ul>
</li>
<li>
The <strong>reduce phase</strong>
<ul>
<li>
Each reducer applies the <strong>reduce function</strong> to its own
partition of data
</li>
<li>
The produced result is stored and replicated three times to
ensure fault-tolerance.
</li>
</ul>
</li>
</ul>
<p>Note that fault-tolerance is easily achieved in this scheme as well: if a
node fails, the name node just re-assigns a task to another node in
the cluster. Also it does not prepare any execution plan beforehand:
once a task is done, the node is assigned to another task. This way it
also achieves load balancing.</p>
<h2>Disadvantages</h2>
<p>This all looks nice, but there are some disadvantages.</p>
<p>There is no declarative high-level language such as SQL. MapReduce implementation in Hadoop is quite a low-level approach and requires writing programs in Java or Python. It
makes queries harder to write and maintain, and this is especially bad for ad-hoc analytical querying.
The solutions to this are high-level SQL-like scripting languages like Pig or Hive, and they are compiled to MapReduce jobs. </p>
<p>There are performance issues: both
Map and Reduce operations are blocking. The reduce phase cannot begin
until the map phase finishes, and this causes performance
degradation. </p>
<p>Finally, Hadoop is still a very young technology, and it does not have decades
of development behind like Relational Databases.</p>
<h1>Hadoop for Data Warehousing</h1>
<p>This article is based on the report written for the "Data Warehouses" course by Toon Calders taken at ULB as a part of our IT4BI curriculum in the winter semester 2013/2014. The original report talks about advantages and disadvantages of using Hadoop in Data Warehousing.</p>
<p>You can find the report at <a href="http://alexeygrigorev.github.io/ulb-dw-project-hadoop/report/report.pdf">http://alexeygrigorev.github.io/ulb-dw-project-hadoop/report/report.pdf</a>, and here's the accompanying slides:</p>
<p>
<center>
<iframe src="//www.slideshare.net/slideshow/embed_code/30384580" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
</center></p>
<h1>Conclusions</h1>
<p>That's all for today. In this post we discussed MapReduce only briefly, but there are more articles on Big Data technologies, and this blog post hopefully gave some background needed to understand them. Now, for example, you can proceed to the article "<a href="http://www.itshared.org/2015/03/naive-bayes-on-apache-flink.html">Naive Bayes on Apache Flink</a>" or other posts labeled with the <a href="http://www.itshared.org/search/label/Big%20Data">Big Data</a> tag.</p>
<p>Stay tuned!</p>Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com1tag:blogger.com,1999:blog-7332477090953898587.post-89996132070901094692015-03-03T22:30:00.000+01:002015-03-04T14:18:31.164+01:00The Dark Side of Entrepreneurship<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgMEcyKcS5ddOi-cht4TaSDvZK4lxNpXOutmrolZXlnmTepFgS4i0hM-gSatbfkums7A8AYnY_HnoB13nmZV-iSIEwMgY8Rg2yC2aJB0sCoGYyJcmG61NG2BhHStweBy4hKU1v1-DcZbVMX/s1600/dark_side.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgMEcyKcS5ddOi-cht4TaSDvZK4lxNpXOutmrolZXlnmTepFgS4i0hM-gSatbfkums7A8AYnY_HnoB13nmZV-iSIEwMgY8Rg2yC2aJB0sCoGYyJcmG61NG2BhHStweBy4hKU1v1-DcZbVMX/s320/dark_side.png" /></a></div><br />
<i>"We will have more than a million clients and our company will be top leader in the industry over the next year"</i>. This is what every first time entrepreneur says at some point in time.<br />
<br />
We often hear stories about young entrepreneurs who dropped school at a very young age and had a huge success. We look at these very few success stories and, as entrepreneurs, we lie to ourselves that one day we will be like them...<br />
<br />
You normally recognise entrepreneurs as those who change jobs very frequently. They try a bit of everything and, in the end, they don’t get deep into any of the topics. They like to taste a bit of everything. They change countries, jobs and friends and it seems that, everywhere they land, they find something to do. They are proactive and extremely curious. They just don’t find their place in any of the traditional companies. They are dreamers and born sellers, even if they have to sell things not even they can imagine.<br />
<a name='more'></a><br />
<h2>How it feels like for the first time</h2>At the beginning it is just you... You continuously think about your business idea and become annoying for others. You could talk for hours about your business, until your friends just get sick of you. Surprised by your confidence and too sick to keep on hearing about this business idea, your friends start joining and you start to believe that you will conquer the world. You sell things that don’t exist yet and you start promising things that you do not even see as feasible.<br />
<br />
You hear nothing else but good things about your company. When someone criticises your idea, you just don’t listen to them. You assume they must be jealous on your success.<br />
You think that you were the only one with this idea and you believe you have a real chance. Jealous people are sending you, from time to time, information about similar companies but your endeavour is different to them because you use a different colour or because you have a new feature.<br />
<br />
You start stalking your competitors and become absolutely obsessed about them. You think that every move they make is a threat to you.<br />
<br />
Instead of talking to your customers, you go in front of investors and start selling them your project, without even a prototype. You ask them to imagine your product and they just laugh in your face but, no worries, investors are mean, what can you expect from them?<br />
<br />
You start becoming arrogant to your team and acting like a boss. You are so stubborn that you start ignoring the warnings and listen only to positive feedback.<br />
<br />
You now have the product! You feel so good seeing it online. The only problem is that bad feedback comes and comes and comes. And no one is paying... You start telling yourself you had bad luck. Your team starts doubting about you. You start doubting about yourself. They smell your fear and go away.<br />
<br />
You suddenly have a finished product, wasted 6 months in developing it and it is perfect only to your eyes. You get frustrated and think that business is just not for you. You knock at the doors of some big corporations and, after a few job interviews, you realise your place is not there...<br />
<br />
<h2>And yet you start another company...</h2>You might think that this is not going to be your story! I am going to have millions of clients and get rich from the first time. But few are the cases when you are successful from the first time.<br />
<br />
No matter what time you ask an entrepreneur how his or her business is going, you always get the same answer: <i>Amazing!</i> We have this new feature, <i>bla bla bla</i>... And he or she starts talking and talking about how well things are going without having even a single client.<br />
<br />
If you want to check the other side of the story and ask investors about their investments, they will always start with the very few ones that were successful. They never tend to talk about <i>"the others"</i>, as they call the biggest failures.<br />
<br />
In societies where entrepreneurship seems to be cool and where everyone’s dream is to have a business, at which point do we have to stop expecting it to be perfect? When are we going to stop seeing <b>failure</b> as defeat instead of <b>as a part of the learning process</b>? When are we going to stop lying to ourselves that first time entrepreneurs have to succeed?Anonymoushttp://www.blogger.com/profile/10392561263600280453noreply@blogger.com0Barcelona, Spain41.3850639 2.173403499999949441.1944764 1.8506799999999495 41.5756514 2.4961269999999494tag:blogger.com,1999:blog-7332477090953898587.post-87142550254586353552015-02-15T12:03:00.001+01:002015-06-19T10:53:38.297+02:00Spring Batch Essentials<div dir="ltr" style="text-align: left;" trbidi="on">
<br />
<center>
<img border="0" src="http://i.imgur.com/v08BYDi.jpg" /></center>
<br />
The book "Spring Batch Essentials" by Packt Publishing is in print now, and I had a pleasure to be a technical reviewer of this book.<br />
<br />
Spring Batch is a tool for creating ETL ("Read/Process/Write") jobs: for batch processing large portions of enterprise data that requires sophisticated transformations and involves complex business logic. It gives you a possibility to manage jobs easily, supports transactions and allows job execution to be scaled to process large volumes of data.<br />
<br /><a name='more'></a>
So if your applications are already running on Java and/or Spring and if you're looking for a way to integrate and batch process the data, Spring Batch is an excellent choice. You can read more on the <a href="http://docs.spring.io/spring-batch/trunk/reference/html/spring-batch-intro.html">Spring Batch's introduction page</a> and, of course, in the book. The book is already available on <a href="https://www.packtpub.com/application-development/spring-batch-essentials">the publisher's website</a> and also in <a href="https://www.safaribooksonline.com/library/view/spring-batch-essentials/9781783553372/">Safari books library</a>. <br />
<br />
<h3>
Table of Content</h3>
<ol>
<li>Spring Batch Fundamentals</li>
<li>Getting Started with Spring Batch Jobs</li>
<li>Working with Data</li>
<li>Handling Job Transactions</li>
<li>Step Execution</li>
<li>Integrating Spring Batch</li>
<li>Inspecting Spring Batch Jobs</li>
<li>Scaling with Spring Batch</li>
<li>Testing the Spring Batch </li>
</ol>
<br />
<ol>
</ol>
<h3>
What You Will Learn</h3>
<ul>
<li>Understand the infrastructure to design, develop, and execute a batch application</li>
<li>Configure batch jobs using XML, Expression Language and annotations, and execute from diverse platforms</li>
<li>Develop batch jobs with the essential read, process, and write features different forms of data</li>
<li>Get to grips with techniques you can use to manage transactions, control job flows, and utilize data sharing</li>
<li>Integrate Spring Batch with other technologies to develop robust batch applications on an enterprise platform</li>
<li>Monitor and report the batch job execution updates by accessing live job execution information</li>
<li>Optimize scale and performance improvement with parallel processing techniques</li>
<li>Perform unit integration and functional testing on Spring Batch applications</li>
</ul>
</div>
Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com1tag:blogger.com,1999:blog-7332477090953898587.post-51322316966535908492015-02-04T20:41:00.002+01:002015-06-20T11:29:48.360+02:00Best Time to Learn Linear Algebra is Now!<center><a href="http://xkcd.com/184/"><img src="http://imgs.xkcd.com/comics/matrix_transform.png"/></a></center>
<br/><br/>
Linear Algebra is a crucial prerequisite for many things, including Statistics, Data Mining, Machine Learning, Computer Vision, Image Processing and many many others, so it's very important to know the basics of Linear Algebra to understand more advanced concepts. For example, it's really helpful for our <a href="http://www.itshared.org/2014/12/it4bi-1st-year-business-intelligence.html">IT4BI studies</a>, especially for the specialization at <a href="http://www.itshared.org/2015/01/it4bi-distributed-and-large-scale.html">TU Berlin</a>.<br />
<br />
And the best time to learn Linear Algebra or refresh your knowledge about it is right now! At this moment there are a couple of nice MOOCs that have just started and a few more are about to start in the nearest future.
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Even if you don't join right now, they should be available in the future for learning as self-paced versions. Additionally I would like to include my favorite video courses on Linear Algebra, they are also for learning at your own speed with no deadlines.
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<h2>Just started</h2>
So I'll begin with the courses that have just started and you still have time to jump in.<br />
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<h3>LAFF, edX</h3>
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LAFF stands for "Linear Algebra - Foundations to Frontiers", and the course page is <a href="https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-02x">here</a>. This is the second run of the course, and the first one was quite successful: many students participated and most of them enjoyed the course.<br />
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This offering started on 28 Jan 2015, but the first deadline is on 16 Feb 2015, so there's still plenty of time to enroll. The estimated effort is 8 hours a week, and the duration is 15 weeks. <br />
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The course includes theoretical videos but all the theory is reinforced with programming exercises. Last time it involved exercises in Python, but this time they switched to Matlab. What is more, MathWorks offered to give free licenses of Matlab to all the participants. This alone is already a good reason to enroll in the course! But, as the teachers say, it's still possible to use python for this, if you wish, because all the coding exercise only require clicking on a "pass" button and it's not actually checked how the exercises are implemented, so it can be any programming environment. <br />
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Additionally, you may download all the lecture notes at <a href="http://www.ulaff.net">ulaff.net</a>. The notes themselves are a very good source of information, even without the videos. <br />
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<h3>Coding the Matrix, Coursera</h3>
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The full name of the course is "Coding the Matrix: Linear Algebra through Computer Science Applications", it's offered at Coursera. The course webpage is <a href="https://www.coursera.org/course/matrix">here</a>. The workload is up to 10 hours per week, and it lasts for 10 weeks.
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This is also a second time when the course is offered, and it follows the same philosophy as LAFF: learning through coding. While the theoretical part is somewhat similar to LAFF, the exercises are different. They also different in a way that there's an automatic checker that verifies your the solutions that you have to submit to the server. The programming environment for this course if Python 3, and it's not possible to use anything else. <br />
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The book that is used for this course has the same title as the course (check the <a href="http://www.amazon.com/Coding-Matrix-Algebra-Applications-Computer/dp/0615880991">amazon page</a> and the <a href="http://codingthematrix.com/">book's page</a>). The author, Philip Klein, promises to offer a more advanced version of this class in the future, so you might want to subscribe for the updates on his website.<br />
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<h3>Linear Algebra, Coursera (Russian only)</h3><br />
If you speak Russian, there's another MOOC that has just stared at coursera. The course website is <a href="https://www.coursera.org/course/linalg">here</a>. I'm not sure if there're many readers who know Russian, but I still would like to mention it, because I think it's nice that Coursera goes very international. It's a first run of this course, so I can't comment on the course content, but you can go check yourself.<br />
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<h2>Start soon</h2>
<h3>Applications of Linear Algebra, edX</h3>
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This is a series of two courses
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<li><a href="https://www.edx.org/course/applications-linear-algebra-part-1-davidsonx-d003x-1">Part 1</a>, starts on 23 Feb 2015 (5 weeks)</li>
<li><a href="https://www.edx.org/course/applications-linear-algebra-part-2-davidsonx-d003x-2">Part 2</a>, starts on 6 Apr 2015 (4 weeks)</li>
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The syllabus looks pretty promising: they put the emphasis on applications and focus on Computer Graphics and Data Mining. The estimated effort for each course is up to 20 hours in total. They unfortunately don't provide a detailed syllabus with all the topics, but it seems complimentary to the basic ones like LAFF or Coding the Matrix.
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<h3>Introduction to Linear Models and Matrix Algebra, edX</h3>
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This is another course at edX, this time offered by Harvard. The course page is <a href="https://www.edx.org/course/introduction-linear-models-matrix-harvardx-ph525-2x">here</a>, it starts on 16 Feb 2015 and takes 2 week.
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This course is a part of a "Life Sciences" series of courses by Harward:
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<li><a href="https://www.edx.org/course/statistics-with-r-for-life-sciences-harvardx-ph525-1x">PH525.1x: Statistics and R for the Life Sciences</a></li>
<li><a href="https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra-harvardx-ph525-2x">PH525.2x: Introduction to Linear Models and Matrix Algebra</a></li>
<li><a href="https://www.edx.org/course/advanced-statistics-for-the-life-sciences-harvardx-ph525-3x">PH525.3x: Advanced Statistics for the Life Sciences</a></li>
<li><a href="https://www.edx.org/course/introduction-to-bioconductor-harvardx-ph525-4x">PH525.4x: Introduction to Bioconductor</a></li>
<li><a href="https://www.edx.org/course/case-study-rna-seq-data-analysis-harvardx-ph525-5x">PH525.5x: Case study: RNA-seq data analysis</a></li>
<li><a href="https://www.edx.org/course/case-study-variant-discovery-and-genotyping-harvardx-ph525-6x">PH525.6x: Case study: Variant Discovery and Genotyping</a></li>
<li><a href="https://www.edx.org/course/case-study-chip-seq-data-analysis-harvardx-ph525-7x">PH525.7x: Case study: ChIP-seq data analysis</a></li>
<li><a href="https://www.edx.org/course/case-study-dna-methylation-data-analysis-harvardx-ph525-8x">PH525.8x: Case study: DNA methylation data analysis</a></li>
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<h2>Self-paced</h2>
If you don't like deadlines, you can take all of the courses above at your own pace, so you don't have to follow the schedule and do all the homework. However there are many other good Linear Algebra video lectures that are self-paced from the very beginning, and I'd like to recommend a couple of them.
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<h3>Linear Algebra at OpenCourseWare (MIT)</h3>
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In my opinion, these are the best Linear Algebra lectures ever recorded. While LAFF and Coding the Matrix are quite good, the way G. Strang presents the material is excellent. It's a live recording of the MIT class, probably quite old, because it often feels like it was digitized from video tapes. You'll find the recordings at <a href="http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/">OpenCourseWare</a>. Alternatively you can just google and find in on YouTube, iTunes university or in many other places. However, while the videos are amazing, you have to do the exercises to strengthen the theory, and this is where other courses come in. So, other courses are complimentary. <br />
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Also, G. Strang has written a nice paper "<a href="http://www.engineering.iastate.edu/~julied/classes/CE570/Notes/strangpaper.pdf">The Fundamental Theorem of Linear Algebra</a>" (or, a fresher one, "<a href="http://web.mit.edu/18.06/www/Essays/newpaper_ver3.pdf">The Four Fundamental Subspaces: 4 Lines</a>") which I recommend to everybody who want to refresh the Linear Algebra knowledge. It's essential for understanding the concepts of Linear Algebra to think in terms of spaces and subspaces, and this article helps a lot to do it with nice pictures which are very helpful for grasping the main ideas. You may also want to check our post about this paper: <a href="http://www.itshared.org/2015/06/the-fundamental-theorem-of-linear.html"> The Fundamental Theorem of Linear Algebra by G. Strang </a> <br />
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<center><img src="http://habrastorage.org/files/7bf/074/ce1/7bf074ce10a147eab74864b6ee3fd352.png" /></center>
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<h3>Linear Algebra, Khan academy</h3>
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Every time I go to google to look for some basic Linear Algebra things, there's always a result from the <a href="https://www.khanacademy.org/math/linear-algebra">Khan Academy course</a>. And the content is very nice: many things are explained in a very nice and understandable way. The videos are nicely categorized and it's very easy to jump over some parts you already know without being lost. It's in some sense also complimentary to Strang's course, because some things are explained differently, although it doesn't have so nice visualization of the subspaces. <br />
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Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com2tag:blogger.com,1999:blog-7332477090953898587.post-71519406690423557242015-01-29T17:39:00.000+01:002015-01-30T10:25:09.318+01:00What to think about when coming up with a BI startup idea<div style="text-align: justify;">
Recently, I did the exercise of creating a business plan, following the single requirement that the business should be focused on Business Intelligence (BI). Almost all businesses nowadays are data driven, i.e. they incorporate some form of BI to drive the company decisions. However, the restriction for us was that our businesses had to deliver information, not only use it internally. </div>
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Now, even though I have been working in the area of BI for the past two years, coming up with a business idea from scratch was not an easy task. In this post I will share with you some of the learning outcomes of this experience, so that if you desire to start up your own BI focused business, you can benefit from what we learned.</div>
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First comes first. The first step to coming up with your business idea is to figure out what area you want to create an impact in. You want to make sure that your business idea is not only relevant now but that it will continue to be so in the upcoming years. The best way to figure this out, is to research the so called <b>MEGA TRENDS</b> that are moving the world. In our research about this, we discovered that one current trend was related to fitness and health, specifically to the need of changing people’s eating habits as a means to reduce public health expenses. We were particularly inclined towards this trend because we were personally motivated by it. Ideally, you should be personally motivated by the impact that your business idea will have, since this will encourage you to stick behind it. </div>
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Once you have identified a trend (or a couple of trends), take a moment to sit down and think of business ideas related to it. This sounds very easy, but the truth is that in our current world of distractions, sitting down, disconnected from all outer stimuli, focusing only on your creativity, requires some effort. Go to a quiet place, turn off your phone and start brainstorming. Take a pen and paper and write down any ideas that come to mind. </div>
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At another moment, go over the ideas from your brainstorming and stick with your favorite one or two. Once you have chosen an idea, it is time to refine it. This means analyzing who your clients will be, studying which competitors exist, and defining what makes you different from them. There are business tools for this like <a href="http://en.wikipedia.org/wiki/SWOT_analysis" target="_blank">SWOT analysis</a> and <a href="http://en.wikipedia.org/wiki/Porter_five_forces_analysis" target="_blank">Porter's analysis</a>. It is a very good exercise either to present this first refined version to someone, or to write it down on a document which you can come back to as your idea evolves.</div>
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Up until now I have talked about the creation of a business idea from a general perspective, not focusing specifically on BI entrepreneurship. Now let's focus on what's important to consider in the BI world.</div>
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The most important point to consider is this. In order to deliver analytics, you need one essential thing: <b>DATA</b>! Your business will depend on it, whether it is generated by your users or it will be provided by (or retrieved from) some supplier. We will analyze the pro's and cons of both approaches:</div>
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<li><b>Data from suppliers</b>. When I refer to this I mean that the data has not been generated by you or your users. A straightforward example of this are news feeds like Flipboard. Flipboard crawls the web to obtain news stories from different suppliers, to provide users access to different sources from a single platform. Similarly, search engines such as Google, store information about web pages, and links between them to provide you the most relevant results to a search query. The information of the webpages and links is not directly input by users to Google, it is obtained by crawling the web. The difficulty of this way of obtaining data is twofold. First and most importantly, you need to figure out a way to transform the data from the suppliers to your data model, and what is more challenging, you need to be able to automatically update data when it suffers changes. The second issue you may encounter is that there may be legal regulations over the use of the data you wish to store.</li>
<li><b>User generated data</b>. The most obvious example of this type of business is Facebook. In this type of schema you do not have to deal with the problems of transforming and updating data, since you decide the format of what users can input. However, since you rely on user data, if you do not get users to interact with your platform, it is unlikely that you will become successful as a business. One of the ideas we had come up with while thinking about our business idea, was to create a platform to allow users to analyze job markets in different cities of the world. However, when realizing we would be directly competing for user data with a giant like LinkedIn, we quickly desisted.</li>
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I mentioned Google as an example of a business which uses data from suppliers. However, this is not really true, since Google's business model is mostly based on advertisements which are personally targeted thanks to user generated data. As a matter of fact, my recommendation is that whatever business idea you think of, try to find a way to combine both data you receive from suppliers and user generated data, since this will mitigate some of the risks associated to using only one approach.</div>
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This raps up my advice for coming up with an innovative business idea for a BI related company. Sounds pretty simple right? Even for those not planning on becoming entrepreneurs, I invite you to do this exercise since it provides a lot of insight as to what it takes to start a business and how the tech world is moving nowadays. Just as a final point, though this is included as part of the SWOT and Porter's analysis, I cannot stress enough the need to think about who will be paying for the service that your business aims to provide. </div>
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In a future post I will cover next steps once you have come up with your business idea.</div>
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Good luck!<br />
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Anonymoushttp://www.blogger.com/profile/09844024931548619141noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-7135089496992611082015-01-28T16:05:00.000+01:002016-04-18T13:26:02.655+02:00IT4BI FAQ<div dir="ltr" style="text-align: left;" trbidi="on">I quite often receive questions about the IT4BI program: my CV is on the program's website, and people see my email there and write me. Also, I get quite a lot of questions from social networks. I usually try my best to give answers to all of them, but many questions repeat again and again, so it's a good idea to create a FAQ and make the answers are available to everybody, so I don't have to repeat myself. A fairly large part of the questions are about the admission process, and I already addressed some of them in <a href="http://www.itshared.org/2015/01/it4bi-how-to.html">IT4BI: How To</a>. <br /><br />
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<b>Important:</b> Before rushing into sending me a message by email or LinkedIn, please do read this FAQ and other <a href="http://www.itshared.org/search/label/IT4BI">pages about IT4BI</a> on this blog! Also, instead of contacting me directly, please use the <a href="http://www.quora.com/IT4BI">IT4BI tag</a> on <a href="http://www.quora.com/">Quora</a>.<br /><br />
I divided the questions into several categories: application, organization, work and studies.<br />
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<h2>Application Questions</h2>
<b>Q</b>: How do I apply? How is the selection process organized?<br />
<b>A</b>: Just read the <a href="http://it4bi.univ-tours.fr/">website</a>, everything is there. Also have a look at our post <a href="http://www.itshared.org/2015/01/it4bi-how-to.html">here</a>.<br />
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<b>Q</b>: What are the strict prerequisites for me to get accepted?<br />
<b>A</b>: You can find them at <a href="http://it4bi.univ-tours.fr/home/students/admission/">the official admission page</a> of the program, the "Eligibility Criteria" section.<br />
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<b>Q</b>: I sent something, but they don't reply, what should I do?<br />
<b>A</b>: There's nothing I can do, sorry. Try to send it one more time, or find them on social networks such as <a href="https://twitter.com/it4bimaster">Twitter</a> or <a href="https://www.facebook.com/it4bi">Facebook</a>. <br />
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<b>Q</b>: I don't have perfect grades. Should I apply anyway?<br />
<b>A</b>: By all means! There are many other factors that are evaluated: e.g. your motivation, your work experience, etc. See the <a href="http://it4bi.univ-tours.fr/home/students/admission/">program website</a> and <a href="http://www.itshared.org/2015/01/it4bi-how-to.html">a post here</a> to learn more about evaluation.<br />
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<b>Q</b>: If I apply earlier, does it increase my chances of getting selected?<br />
<b>A</b>: No, I highly doubt that the application date has any impact on the selection decision, as long as you meet the deadline.<br />
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<b>Q</b>: What is your average mark in your diploma?<br />
<b>A</b>: Even though my average mark is quite high, don't let it discourage you, just apply.<br />
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<b>Q</b>: How important is to have a good IELTS/TOEFL score?<br />
<b>A</b>: I don't think it's crucial: it looks to me as a pass/fail criterion, so you have to be above the threshold. Also I know that some of my classmates were admitted conditionally without a certificate, on condition that they would provide it afterwards. But, of course, the higher your score is, the better. <br />
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<b>Q</b>: What is your IELTS/TOEFL score?<br />
<b>A</b>: As I wrote, I don't think it matters a lot, but if you are really interested in my and my classmates IELTS and TOEFLs scores, you can check our CVs at <a href="http://it4bi.univ-tours.fr/home/students/">the IT4BI website</a> - many of us have the score on CVs.<br />
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<b>Q</b>: My IELTS/TOEFL score is lower than the threshold. Should I apply?<br />
<b>A</b>: You may try, and as far as I know you might be accepted conditionally: on condition that you will retake the test and get the needed score.<br />
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<b>Q</b>: How long did it take to prepare all the application documents?<br />
<b>A</b>: A couple of months: taking IELTS, translating all the documents to English and writing a motivation letter takes a lot of time.<br />
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<b>Q</b>: I'm still doing my bachelor's. Can I apply?<br />
<b>A</b>: Yes, provided that you'll have received your bachelor diploma by the end of this academic year.<br />
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<b>Q</b>: What to write in Motivation Letter and CV? What my references should write in Recommendation Letter?<br />
<b>A</b>: Check <a href="http://www.itshared.org/2015/01/it4bi-how-to.html">IT4BI: How To</a>.<br />
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<b>Q</b>: I'm on the Waiting List!<br />
<b>A</b>: No worries, it doesn't mean that you are not going to get in with certainty, it just means that there could be a delay in getting it. It happened to me as well, yet I'm here. But of course, it doesn't mean that you'll get it, so prepare some alternative options.<br />
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<b>Q</b>: Did you apply to other programs or just to IT4BI?<br />
<b>A</b>: Yes, I applied to other programs as well. You can read about the programs I applied to in <a href="http://www.itshared.org/2015/01/european-master-programs-in-data.html">European Master Programs in Data Analysis</a>.<br />
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<b>Q</b>: What if you hadn't been offered a scholarship?<br />
<b>A</b>: I had other nice programs to select from, so I would have chosen another one.<br />
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<b>Q</b>: Can you check my Motivation Letter, CV, IELTS Essays, etc? <br />
<b>A</b>: Sorry, but no, I will not do this. <br />
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<h2>Selection Procedure</h2>
<b>Q</b>: Is there an interview?<br />
<b>A</b>: No, there is no interview. At least there was no interview when I applied.<br />
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<b>Q</b>: What's the main reason you were selected? <br />
<b>A</b>: Honestly, no idea. I graduated from a very small and not famous university in Far East of Russia, and compared to some other people in my group, I don't have strong BI professional background. Even though I did have some software engineering working experience, many people in my group didn't, so again I don't think it was very important. If I have to hazard a guess, I'd say that my motivation was the reason, and maybe all the rest (recommendations, education, work experience, etc) just reinforced it.<br />
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<b>Q</b>: Did you have working experience? What kind of working experience did you have when applying?<br />
<b>A</b>: Yes, as I wrote, I did have some working experience: about 3 years of software development (mostly Java) at not even remotely BI-related positions. <br />
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<b>Q</b>: I have no working experience. Should I apply? <br />
<b>A</b>: Of course you should. Many people who study with me have no industrial working experience, and yet they were selected.<br />
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<b>Q</b>: I have a lot of working experience. Should I apply? <br />
<b>A</b>: I don't see how this may be a problem, especially if the experience is CS/BI related. To me, it's the opposite: I believe it's a great advantage and you should use it.<br />
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<b>Q</b>: I already got a Master degree. Is it an advantage or disadvantage? <br />
<b>A</b>: Even the website says that it's an advantage, so no worries. I even heard that for some programs (not IT4BI) people with PhD degrees got scholarships. I once read a forum post of one Russian lady who was genuinely surprised about getting selected: she thought she was overqualified for a scholarship and was planning to pay money for her studies. So, just be sure to mention your previous degree in the cover letter and give a motivation why you think you need a second master.<br />
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<b>Q</b>: I have some publications. Is it a plus? Should I mention them?<br />
<b>A</b>: Yes, I definitely think it's a plus and you should mention them in your motivation letter and CV.<br />
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<b>Q</b>: I tried to apply to IT4BI last year and I got rejected. Should I try this year?<br />
<b>A</b>: There is no harm in trying it one more time. But I wouldn't send just the same application as previously, and instead I'd rework the motivation letter and include all the achievements over the past year.<br />
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<b>Q</b>: What do you think I should do in one year before applying to improve my chances of getting a scholarship?<br />
<b>A</b>: I would start learning things I'm interested in myself: there are plenty of MOOCs and other free resources, so begin using them now. And don't forget to mention this in your motivation letter when you write it: I believe it will show that you want to achieve your goals no matter what. And even if at the end you don't get accepted, you will already be moving towards your aim.<br />
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<h2>Logistics / Organization</h2>
<b>Q</b>: Can I go with my wife/husband?<br />
<b>A</b>: Yes, you can, although the process of getting visas for each country is very hard, it's not impossible: my wife joined me in my studies from the very beginning and she and I are both not European. The coordinating university (ULB) helped us a lot with all the bureaucracy and paperwork for getting to Belgium and it was quite an undertaking. Once we were in Belgium, getting a French visa is way easier. After that, getting a German visa is little bit trickier, but it's also possible. For Spain, from what I heard, it's quite harder, but I think nothing can be more difficult than Belgium.<br />
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<b>Q</b>: Do they provide housing? Is it good?<br />
<b>A</b>: Yes they do provide. One year ago I would answer no, the housing in Brussels is not very good, but now, to my knowledge, the 3rd generation had quite nice rooms. However, I should add that if you're going to live with your partner (husband/wife), then in France they won't allow you to live in a student residence, so in our case, we had to find an apartment on our own. But we had no problems with housing in Belgium and Germany and the universities were very helpful. In any way, if need be, finding a flat is not that difficult.<br />
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<h2>Working as a Student</h2><b>Q</b>: Can I work while studying? <br />
<b>A</b>: If you are a self-funded student, then yes, you can work officially. If you're not a self-funded student and you receive a scholarship, it's possible to work unofficially, but keep in mind that the workload is sometimes pretty dense. See more information about workload below. Also, if you take the specialization at TU Berlin, it is possible to work part time at the university.<br />
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<b>Q</b>: Can I have internship during studies? Will they find me some internship? <br />
<b>A</b>: Yes, you certainly can, and it's even encouraged. However, very few of us (students of 2nd generation) had an internship, even though we could. Also I know that some people worked during the summer break (including me), but wasn't an internship, it was just usual work. The IT4BI committee will not find you internships, but they may give you some useful advice on how to get it. <br />
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<h2>IT4BI Program, Studies</h2>
<b>Q</b>: Are all classes in English?<br />
<b>A</b>: Yes, all classes are in English, maybe apart from the language classes. <br />
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<b>Q</b>: Do you like the program? Have you learned a lot?<br />
<b>A</b>: Yes, I do like the program. Not everything was perfect, not everything met the expectations and I didn't like some things, but in retrospect, yes, I think this is a very good program. I met many new interesting people and I learned quite a lot of new and interesting stuff. I would sometimes disagree with the choice of some of the courses that we had, but at the end I just concentrated on things I like and didn't pay lots of attention to things I don't like. You can read what we studied during the first year in <a href="http://www.itshared.org/2014/12/it4bi-1st-year-business-intelligence.html">IT4BI First Year: Business Intelligence Fundamentals</a>, and during my specialization at TU Berlin in <a href="http://www.itshared.org/2015/01/it4bi-distributed-and-large-scale.html">IT4BI: Distributed and Large-Scale Business Intelligence</a>. <br />
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<b>Q</b>: Is it easy? <br />
<b>A</b>: It's not. Maybe it's just my poor time management, but for me the workload was sometimes pretty high, and it felt especially dense during the 2nd semester because of countless project presentations. The first semester was not as hard as second, but still towards the end of the semester it was quite heavy. However, the 3rd semester is more relaxed as we have more independent type work at TU Berlin like reading papers, coding some algorithms and the like (you can read about it <a href="http://www.itshared.org/2015/01/it4bi-distributed-and-large-scale.html">here</a>). But if you feel that it's not enough for you, then go take the Machine Learning classes by professor Muller, or challenge yourself in some other way.<br />
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<b>Q</b>: Is it more practical or theoretical? Is there a lot of programming? <br />
<b>A</b>: It's more practical and we did a lot of things, but during the 1st year few of them involved programming: it was mostly BI-related things like creating Data Warehouses, modeling Business Processes or creating Data Mining flows. However, the semester at TU Berlin is quite the opposite - here we code a lot! There is only one class that doesn't involve programming, and it's about reading a scientific paper and presenting it to your classmates. <br />
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<b>Q</b>: What should I know to be successful in the program? Do I need to be proficient at programming? What about math?<br />
<b>A</b>: Yes, I believe that good programming skills are necessary to get the most out of the program. We mostly used Java, so some familiarity with the language should be helpful. On the other hand, I don't think you'd need a lot of math for IT4BI classes, just basic Calculus and Probability should be sufficient. Also some Linear Algebra wouldn't hurt.<br />
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<b>Q</b>: Is it a data science program? <br />
<b>A</b>: To some extent, yes, it is, depending on how you define "data science". It definitely has a lot to do with data analysis, but I would say that the 1st year is more devoted to "enterprise data"-oriented analysis, i.e. Business Intelligence. To make it a real "Data Science" program, I would exclude a couple of courses in favor of more Data Science ones. For example, I'd include Machine Learning classes from TU Berlin (the ones that we actually can attend, but for no credit), Advanced Statistics and the like. However there are many classes that do cover Data Science related things - specifically, Information Retrieval, Data Mining, Scalable Data Mining, etc. Additionally, although many remaining coursers are not "Data Science" per se, they are still related: e.g. Data Warehousing, Decision Engineering, Database Systems Architecture, etc.<br />
And actually, I recently attended a talk by Professor V. Markl, the head of the DIMA group at TU Berlin, and he mentioned that "it's important to teach future data scientists" when we was talking about their contribution to the IT4BI program. So, from his point of view, we are "data scientists", whatever he meant by that.<br />
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<br />
<h2>After Graduation</h2>
<b>Q</b>: What are career prospects for IT4BI graduates? <br />
<b>A</b>: For me it's too early to say about the impact on my future career and how different it would be if not the program, but for sure I can say that all IT4BI students of the 1st generation who I have in my LinkedIn network already work: they graduated in September 2014, and now it's January 2015.<br />
<b>Update:</b> It's August 2015 now, just one month before our graduation, and I know that out of 8 people who decided to go to TU Berlin, 6 are in Berlin and all 6 have already found jobs. So in Berlin there are a lot of opportunities and IT4BI graduates are welcome here. Also, many of us are not EU citizens, yet there were no problems with finding a job.<br />
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<b>Q</b>: Can I get a job seeker visa after graduating?<br />
<b>A</b>: Yes, if you graduate from a German university, for Germany you can get a job seeker visa for 18 months. If you graduated from other university outside of Germany, if this university is recognized by the German ministry of education, you can get a similar visa for 6 months. I am no aware of similar visas in France and Spain. Also note that none of us (those who chose the TUB specialization) actually needed this kind visa, we all found a job before graduating.<br />
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<h2>Other Questions</h2>
<b>Q</b>: Can you recommend me any other data analysis related Master programs?<br />
<b>A</b>: Yes, I can, please have a look at <a href="http://www.itshared.org/2015/01/european-master-programs-in-data.html"> European Master Programs in Data Analysis</a>. I am not aware of other Master programs in this field, sorry. I also don't know anything about programs in the USA.<br />
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<b>Q</b>: How can I become a data scientist? <br />
<b>A</b>: First of all, consider applying to IT4BI or some other program in data analysis (for example, in one of the programs described in <a href="http://www.itshared.org/2015/01/european-master-programs-in-data.html">this post</a>). Also there are so much material out there available online for free, so make use of it! I may try to address it in some later posts, so stay tuned.<br /><br />
<b>Q</b>: I have more questions about IT4BI, how I can ask them? <br />
<b>A</b>: There's an <a href="http://www.quora.com/IT4BI">IT4BI tag</a> that I follow at <a href="http://www.quora.com/">Quora</a>, so you're welcome to ask any questions there, just be sure to tag them and ask me to answer. Alternatively, you can try to find my email and ask me directly. But it would be better to do it publicly so other people will also benefit from the answer. So please do use the tag instead of contacting me personally.<br />
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<div align="right"><i>Last update: 18.04.2016</i></div>
</div>
Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com2tag:blogger.com,1999:blog-7332477090953898587.post-73162972836012440772015-01-24T10:36:00.000+01:002015-06-19T10:54:13.298+02:00IT4BI: How To<div dir="ltr" style="text-align: left;" trbidi="on">
In the post <a href="http://www.itshared.org/2015/01/european-master-programs-in-data.html">European Master Programs in Data Analysis</a> I listed some Master programs that I myself applied to. There I also briefly outlined how to apply to them. In this post, I would like to give more details about the process of applying to the program of my choice: Erasmus Mundus IT4BI. This is mostly based on my experience and I would like to share this with you. <br />
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Additionally, I sometimes get questions by email about the program and quite a lot of them are about the process: documents, motivation letters, etc. In this post I will also address these questions, and spare myself the troubles of typing the same text over and over again :) <br /><br />
<a name='more'></a>
<h2>
Admission</h2>
The <a href="http://it4bi.univ-tours.fr/">official IT4BI website</a> has quite a lot of valuable information, so first and foremost, be sure to read the <a href="http://it4bi.univ-tours.fr/home/students/admission/">Admission page</a> and the <a href="http://cs.ulb.ac.be/emundus/images/stories/provided_documents/it4bi_application_manual.pdf">Application manual</a>: it has all needed information about the process. <br />
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Then you register at <a href="http://cs.ulb.ac.be/emundus/">http://cs.ulb.ac.be/emundus/</a>, and follow the steps of the wizard. <br />
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Some of the documents that you'll need to submit: <br />
<ul>
<li>Motivation Letter</li>
<li>CV</li>
<li>Bachelor's diploma and transcript</li>
<li>Self-assessment form of studied subjects</li>
<li>Proof of English proficiency</li>
</ul>
So, let's now take a closer look at some of the documents that you'll need to submit. <br />
<br />
<h3>
Motivation Letter</h3>
As it says on the registration page, a cover letter should "state your interest, your possibilities in the Master, and anything you consider relevant to know for the Selection Committee", so this document may contain anything, and no wonder it's not always clear what to include there. <br />
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For me it was the most challenging part: it wasn't something that I could get over with just in one evening. And I believe you shouldn't do this: take your time to brainstorm ideas, shape them, proofread and rewrite it several times until you like the results. <br />
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I believe that you should include the following in the letter: first, say who are you, what is your academic and professional background, what are interesting challenges that you faced during your studies or work that led you into considering applying to IT4BI. Then, of course, say why do you want to apply, what are your goals and what the program will give you to achieve them. Lastly, you may mention your plans after you graduate from IT4BI. I also think that you should include any multicultural experience that you had, and, more importantly, what you can bring to the program and what you can share with your classmates in the future. <br />
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This was the outline of my motivation, and you may or may not agree to some of these points, and it's fine, because I believe that it's a very personal document, and it's up to you to show your motivation in a way that you think is the best. <br />
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The length of my motivation letter was 2 pages. Some people say that 2 pages is too long and there must be only one page. But eventually this is for you to decide. <br />
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From what I heard, the letter has more impact than any other document, so take it seriously: proofread it, discuss with your teachers, colleagues, friends, ask them to review it. I also showed the letter to my English teacher who helped me to nail down some phrases that needed reshaping. <br />
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And finally, please don't ask me to show my motivation letter. It's a personal document, and it will not be very useful for you. And I will not help you with your motivation letter, sorry: as I said, I suggest to discuss it with your teachers, colleagues and friends. They know you better than I and they will be able to help you. <br />
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<h3>
CV</h3>
If you contacted me by email that you found in my CV from the IT4BI website, then you can get the idea of how my CV looked like when I applied. Although it's not the CV that I used for applying to the program, the content of mine was almost the same. Also, the CV has to be in the <a href="https://europass.cedefop.europa.eu/en/documents/curriculum-vitae">Europass CV format</a>. It's pretty easy to create CVs there: you just follow the template and that's it. Also, if you haven't done it already, you can check CVs of my classmates at the <a href="http://it4bi.univ-tours.fr/home/students/">Students page</a> of the IT4BI website - many of them use this format. <br />
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From what I remember there should be some constraint on the length of your CV, so be sure to verify that as well. <br />
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Finally, last remark: please don't ask me to check your CV. However, if you do want to get it checked, there are plenty of professional forums where you can ask for help, and you find them by googling. And again, ask your colleagues or teachers, they are a very good source of help. <br />
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<h3>
Recommendation Letters</h3>
When you register at the website, you enter the emails of your references and, after that, they receive a MS Word document to fill - like <a href="https://drive.google.com/file/d/0B5_owBeRez4bN3BUN1lINmdzTE0/view?usp=sharing">this one</a>. <br />
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However the template (as you see) indicates that the recommendation applies only for students, so it seems that it can only be filled by academic staff. This is probably because they expect to receive recommendations from former teachers and professors. In my case, however, I wasn't a student anymore and I lived far away from my university, so it was quite hard for me to get in contact with my old teachers. Instead I asked my former boss and my colleague to give me letters of recommendation, so 2 of the 3 references in my case were work related, and only one was from my teacher. As for the template, they just didn't fill the parts about student performance, and described the job I did. <br />
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It might be that your references are willing to help, but they don't know what to write. Since you know the template, you can give them some hints. For instance, for number 3, "How does the applicant stand out in academic and personal terms and how would you assess his/her potential", it will probably not be enough to write something like just "he was an excellent student", but also give some details. For example, the dean described all my extra-curriculum activities at the university like participating in math and programming contests and some awards that I received during the studies. My colleague, on the other hand, described the problems that I was helping the team to solve and my technical skills. He also added that I'm interested in data analysis and like playing with data. <br />
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<h3>
Self-Assessment Form</h3>
This is a form where you put all the classes that you had during your bachelor's degree and the credits that you received for them. The spreadsheet converts them to ECTS credits. This document looks crazy, and I remember struggling with it. No wonder that I sometimes receive questions about it and people ask me to show my form. <br />
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My experience with this form will probably be helpful mostly for people who have similar system of education as in Russia. For me it was difficult because in Russia we don't have a credit system, but instead we measure the effort of each class directly in hours. So I eventually just put the amount of hours in "original credits". Also at home there's no such thing as "minimum credits to graduate", so I just summed over all my hours and put it into "minimum credits to graduate". Finally, we have two kind of marks: for some subjects we have just pass/fail, and for some we get marks 3, 4 or 5, with 5 being the best. For the first type of mark I just left the cells empty, and put the actual mark only for the second type. I wrote down all the decisions that I made and add this as a comment to the form submission. <br />
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And <a href="https://docs.google.com/spreadsheets/d/1YtyYllJRzMSTLilew97pVP3A3ZNZCTmDKSO8nWCX2EA/pubhtml">here</a> you can have a look at my form. <br />
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<h3>
Other Things</h3>
To prove your working experience, you will need to contact your current/former employers and ask for a confirmation that you worked for them. In some countries, including Russia, people have <a href="http://en.wikipedia.org/wiki/Employment_record_book">employment record books</a>, so if you have such thing, you may submit it as well. I did submit mine, but I didn't get the notarized translation, as it was quite expensive, so I decided to see if the IT4BI committee asked for it, but they didn't. <br />
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Additionally, you may take advantage of the career center at your university - they may help you with preparing the documents. I know that some of my classmates did it when applying for the program, and as I understood, quite successfully. <br />
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<h3>
Evaluation</h3>
Once you submit your application, it will be scored according to the following criteria (taken from the IT4BI website): <br />
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<ul>
<li>Domain of the bachelor: Points will be assigned proportional to the percentage of Computer Science credits in the curriculum.</li>
<li>Academic results: This will be calculated as the weighted average of the marks in the different subjects.</li>
<li>Other studies: A second bachelor or master will score depending on the obtained credits in computer science. Extra-curricular courses taken will be scored based on lecturing hours taking.</li>
<li>Professional experience: Points will be given by year of work on IT or BI position (regarding the position held and the topic).</li>
<li>Personal project: Points will be assigned based on the following categories: organisation, sequencing, content, vocabulary and word choice, critical thinking, originality and creativity, accuracy of facts.</li>
<li>Reference letters: Points will be assigned based on the approval, comments and reputation of the referees, as testified by the reference letters and the referees’ H-factor.</li>
</ul>
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By personal project, I believe, they mean the motivation letter. Just keep this criteria in mind when preparing you documents. <br />
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<h3>
Waiting List</h3>
You may end up in a waiting list. Don't worry! It happened to me as well, but eventually I made it to the program, and so may happen to you. <br />
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<h2>
Finale</h2>
Even though this post is about IT4BI, it might be useful for other programs as well. I hope it helped! If you have some other questions about our personal experience in the process of applying to IT4BI, you are welcome to ask in the comments. Of course, we will not be able to answer organizational questions, so in some cases it's better to ask the IT4BI consortium directly.<br />
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Whether you're applying to IT4BI program or you just accidentally landed on this page from a search engine, you might be interested in our posts about the program's content: <a href="http://www.itshared.org/2014/12/it4bi-1st-year-business-intelligence.html">IT4BI First Year: Business Intelligence Fundamentals</a> and <a href="http://www.itshared.org/2015/01/it4bi-distributed-and-large-scale.html">IT4BI: Distributed and Large-Scale Business Intelligence</a>. If you have some other questions about IT4BI and they are not related to admission, check <a href="http://www.itshared.org/2015/01/it4bi-faq.html">IT4BI FAQ</a>.</div>
Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com1tag:blogger.com,1999:blog-7332477090953898587.post-55978461026613433222015-01-15T09:11:00.000+01:002015-12-30T09:41:17.093+01:00European Master Programs in Data Analysis<div dir="ltr" style="text-align: left;" trbidi="on">
Some time ago, in 2012, I decided that I wanted to continue my education and get a Master's Degree. I wanted to get more involved into data analysis and related things like machine learning or data mining. Secondly, this had to be a program in Europe because I didn't want to travel too far - back then I lived in Poland. And lastly, I wanted to find a program with both tuition waivers and monthly allowances to cover living expenses.
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I started actively looking for programs that met these criteria and now I would like to share my experience. In this blog post I list the most interesting programs (to me) and I include only ones that I myself applied to. I will not describe the programs/scholarships in details, but instead will refer to links with information. However I will add some things that I think are important: e.g. the process of applying, interesting details, etc.
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<a name='more'></a>
So, here is the list of the programs.
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<h2>
Erasmus Mundus</h2>
This is a set of programs that are funded by European Commission, and their main idea is multi-cultural exchange through mobility: there are students from all over the world and you have to study at several universities in different European countries. There are many Erasmus Mundus programs and all of them have good (probably the best in Europe) scholarships. You can see all the programs <a href="http://eacea.ec.europa.eu/erasmus_mundus/results_compendia/selected_projects_action_1_master_courses_en.php">here</a>.<br />
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It is possible to apply to at most 3 Erasmus Mundus programs, and out of them I selected two most interesting programs for me: IT4BI and DMKM. There are others that are also interesting, for example, Robotics and NLP/Linguistics, but I didn't end up applying to them.
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<h3>
Information Technologies for Business Intelligence</h3>
<b>Website</b>: <a href="http://it4bi.univ-tours.fr/">http://it4bi.univ-tours.fr/</a><br />
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<b>Description</b>: Business Intelligence encompasses many scientific and technological fields including data warehouses, data mining, content analytics, business process management, visual analytics, and requires competences in information systems, web science, decision science, software engineering, and innovation and entrepreneurship. The Erasmus Mundus Joint Master Degree’s Programme in Information Technologies for Business Intelligence (IT4BI) is designed to provide understanding, knowledge and skills in this broad scope of fields. Its main objective is to train computer scientists who understand and help develop the strategies of modern enterprise decision makers.
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So it is about Data Analysis, but with the emphasis on Enterprise data (to some extent).<br />
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There are 3 specialization for this program:
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<ul>
<li>Decision Support and Business Intelligence </li>
<li>Service-Oriented Business Intelligence</li>
<li>Distributed and Large-Scale Business Intelligence</li>
</ul>
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Documents that you need:
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<ul>
<li>diploma and transcript translated to English and notarized</li>
<li>some form with self-assessment of the things you studied at your bachelors</li>
<li>a motivation letter</li>
<li>3 reference letters</li>
<li>English certificate</li>
<li>your CV</li>
</ul>
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<b>Process</b>: first, you register at the website, and upload all the documents. You also enter emails of your reference, so they can receive some special form and upload the references for you. Once you have done everything online, you then send all the documents by post.<br />
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<h3>
Data Mining and Knowledge Management</h3>
<b>Website</b>: <a href="http://www.em-dmkm.eu/">http://www.em-dmkm.eu/</a>
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<b>Description</b>: Data Mining and Knowledge Management (DMKM) has become essential for improving the competitiveness of businesses and increasing access to knowledge. DMKM still comes up, however, against major scientific and technological obstacles. This European Master in DMKM proposes specialized training in this field.
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This is a program about in-depth data mining and data analysis, and related things such as optimization, biostatistics, machine learning, etc.
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There are several specializations:
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<ul>
<li>E-Science</li>
<li>Data Mining and Complex System Modeling, Application in Social Sciences</li>
<li>Knowledge and Decision</li>
<li>Statistical Modeling and Data Mining </li>
<li>Semantic Web</li>
<li>Relational Data Mining</li>
</ul>
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You can have a closer look at the list of courses at <a href="http://www.em-dmkm.eu/59-EN-Courses_content">the program's website</a>.<br />
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The list of documents is similar to IT4BI, but, two of 3 references must be your teachers from prior education and there's no self-assessment form. The process is also similar, but you don't need to send the documents by post before the final decision, everything can be done online.
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Additionally the DMKM committee organizes a skype interview with candidates where they discuss different things with potential students, but mostly they ask things based on the candidate's CV, past education and work experience. They won't ask you specific technical details about complex algorithms like on a job interview - but instead they'd rather like to get to know you better.
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When I was preparing to the skype interview, I found this <a href="https://erasmusum.wordpress.com/2012/02/20/dmkm-interview/">blog post</a> very useful - it's written by a Turkish guy who eventually got admitted to the program. You can also read his blog at <a href="http://erasmusum.wordpress.com/">http://erasmusum.wordpress.com/ </a>- you can find there more information about DMKM. I followed his idea to create a transcript of the interview, and you can find mine <a href="http://pastebin.com/1ef6KeSV">here</a>.
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<h2>
EIT ICT Labs Master School</h2>
<b>Website</b>: <a href="http://www.masterschool.eitdigital.eu/programmes/overview/">http://www.masterschool.eitdigital.eu/programmes/overview/</a>
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This is another set of programs funded by the European Commission and it's very similar to Erasmus Mundus, but with Entrepreneurship minor - instead of languages and multicultural exchange. The scholarships are comparable to Erasmus Mundus, but not as good.
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The process is the same for all EIT ICT programs, so here is it. First, you need to register at <a href="http://universityadmissions.se/">http://universityadmissions.se/</a>, but note that you don't have to pay application fee there.
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Then you need
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<ul>
<li>letter of motivation where you have to describe some entrepreneurial idea. I described possible usages of Twitter for stock price prediction </li>
<li>two letter of recommendations</li>
<li>your CV</li>
<li>diploma and transcript, translated to English and notarized</li>
</ul>
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All the documents have to be sent by post.
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As far as I remember it is possible to apply at most to two such programs, although I might be wrong. I applied to Cloud Computing and Services, but recently I learned that there's a new program (just appeared at the end of 2014) - Data Science, so I'll also add a link to it.
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<h3>
Cloud Computing and Services</h3>
<b>Website</b>: <a href="http://www.masterschool.eitdigital.eu/programmes/ccs/">http://www.masterschool.eitdigital.eu/programmes/ccs/</a><br />
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<b>Description</b>: Cloud Computing and Services will provide students with a system of knowledge both in formal foundations, technological platforms and practical skills in implementing distributed software applications. The programme will also provide an insight into current and future directions of the distributed software development.
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Since I was interested in Data Analysis, I found following specializations interesting:
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<ul>
<li>Distributed Information Management</li>
<li>Cloud & Data Analytics</li>
</ul>
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<h3>
Data Science </h3>
<b>Website</b>: <a href="http://www.masterschool.eitdigital.eu/programmes/dsc/">http://www.masterschool.eitdigital.eu/programmes/dsc/</a>
<b> </b><br />
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<b>Description</b>: The newly established Data Science Master's offers a unique academic programme, whereby students can study data science, innovation, and entrepreneurship at leading European universities. In this programme, students will learn about scalable data collection techniques, data analysis methods, and a suite of tools and technologies that address data capture, processing, storage, transfer, analysis, and visualization, and related concepts (e.g., data access, pricing data, and data privacy).
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This is a new one that didn't exist when I was looking for a program. But had it existed, I would have applied to it for sure.
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All of these specializations are related to data analysis:
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<ul>
<li>Distributed Systems & Data Mining for Really Big Data</li>
<li>Multimedia & Web Science for Big Data</li>
<li>Design, Implementation, and Usage of Data Science Instruments</li>
<li>Process Mining in High Tech Systems, Healthcare, Visual Analytics, or Big Software</li>
<li>The Internet of Things</li>
<li>Big Data in the Cloud</li>
</ul>
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The university where I study now, TU Berlin, hosts the "Design, Implementation, and Usage of Data Science Instruments" specialization which I highly recommend!
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<h2>
Visby Scholarship</h2>
<b>Website</b>: <a href="https://studyinsweden.se/scholarship/visby-programme-scholarships-for-masters-level-studies/">https://studyinsweden.se/scholarship/visby-programme-scholarships-for-masters-level-studies/</a><br />
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This is a scholarship for studying in Sweden that also covers both tuition fees and living costs, but this program is only for candidates from Belarus, Georgia, Moldova, Russia and Ukraine. However, there are similar programs for other countries - be sure to check <a href="https://studyinsweden.se/scholarships/">https://studyinsweden.se/scholarships/</a>
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For Visby scholarship, the process is the following: first, register at <a href="http://universityadmissions.se/">http://universityadmissions.se/</a> (like for EIT ICT Labs) but here you have to pay the fee. After that you may select several Master programs from different universities across Sweden, but the Visby scholarship applies only to one of them, so be sure it's your first choice. At certain period of time you also register at the scholarship page and upload all needed documents.
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<br />
Documents are the usual ones: diploma and transcript, translated to English and notarized, an English certificate, reference letters. But the motivation letter is different: you have to describe how your home country will benefit from your studies, and how the scholarship will affect your personal and professional development. In my case I said that I wanted to become a data scientist and then go back to my homeland and spread the knowledge there.<br />
<br />
I selected a Business Intelligence program at Dalarna University, but there are other good possible choices at KTH and other well-known Swedish universities. Just check the list of programs that are covered by the Visby scholarship on the website.
<br />
<br />
<h3>
Business Intelligence at Dalarna University</h3>
<b>Website</b>: <a href="http://www.du.se/en/Study-at-DU/Programmes/Business-Intelligence-/">http://www.du.se/en/Study-at-DU/Programmes/Business-Intelligence-/</a><br />
<br />
<b>Description</b>: The BI Program gives an intensive and broad training in data collection, data processing, information analysis, information modelling, decision making - so called a BI chain training system - which integrate the core components from AI, Business Data Analysis, Information Systems with Statistics principles.
<b> </b><br />
<br />
<b>Syllabus</b>: <a href="http://www.du.se/en/Study-at-DU/Programme-syllabus/?programkod=DMBIA">http://www.du.se/en/Study-at-DU/Programme-syllabus/?programkod=DMBIA</a>
<br />
<br />
<h2>
Others</h2>
<h3>
Eindhoven University, ALSP</h3>
At TU/e there is a scholarship that waives the tuition fee and also covers living expenses - it's called "Amandus H. Lundqvist Scholarship Program". The idea is that some company sponsors your education and then, after graduating, you have to work at this company for some amount of time. They claim that it doesn't affect the salary that you'll receive while working, so it looks quite promising: first you study and then there's a work placement awaiting you. You can read <a href="https://www.tue.nl/studeren/studeren-aan-de-tue/studiekosten/studiefinanciering/amandus-h-lundqvist-scholarship-program/">here</a> more about the scholarship.
<br />
<br />
There are two master programs at TU/e that were interesting for me: <a href="https://www.tue.nl/en/education/tue-graduate-school/masters-programs/business-information-systems/">Business Information Systems</a> and <a href="https://www.tue.nl/en/education/tue-graduate-school/masters-programs/computer-science-and-engineering/">Computer Science and Engineering</a>. There are some core subjects, and the student can select the rest.
<br />
<br />
The <b>process</b>: you first apply to a master program and select that you also apply to the scholarship. The list of documents to upload and send usual: diploma, transcript, 3 references, a motivation letter and CV. Then you wait until they accept you to the master program, and only after that they initiate the scholarship procedure.
<br />
<br />
Here it is (copied from one of the emails from TU/e):
<br />
<ol>
<li>The Department/Faculty Admissions Board nominates candidates.</li>
<li>The TU/e Scholarship Selection Committee (the central selection board that compares nominations from all faculties) decides which candidates may enter stage 3.</li>
<li>Selected students complete a questionnaire on their personality and characteristics.</li>
<li>The answers to the questionnaires are analysed and the TU/e Scholarship Selection Committee will decide which students will be offered an ALSP scholarship.</li>
</ol>
<br />
I must have dome something crazy at the stage 3 with the personality questionnaire so they decided to have a phone call with me and see how adequate I am :-) Also, once you receive the final offer, you will be asked to transfer a guarantee payment, but this money will be used for securing the accommodation and other stuff.
<br />
<br />
<h3>
University of Westminster, Full Fee scholarship</h3>
The University of Westminster in London offers a full tuition fee waiver plus covering of living expenses for international students who want to get a Master's degree. You can read more <a href="http://www.westminster.ac.uk/study/prospective-students/fees-and-funding/scholarships/postgraduate-scholarships/westminster-full-fee-scholarships">here</a>. But it's just one possible scholarship, and they have quite a lot of them - see <a href="http://www.westminster.ac.uk/study/prospective-students/fees-and-funding/scholarships/international-postgraduate-scholarships">here</a>.
<br />
<br />
The process is similar: apply to the program, upload documents (diploma, references, motivation letter) and then apply to the scholarship.
<br />
<br />
I decided to apply to <a href="http://www.westminster.ac.uk/courses/subjects/business-information-systems/postgraduate-courses/full-time/p09fpbua-msc-business-intelligence-and-analytics">Business Intelligence and Analytics program</a>.
<br />
<br />
<h2>
There's More</h2>
This list is just a list of programs to which I applied myself (except for EIT Data Science). But there's more:
<br />
<ul>
<li><a href="http://www.studyinholland.nl/scholarships/grantfinder">Scholarships in the Netherlands</a>. Check good programs at the University of Amsterdam </li>
<li><a href="http://www.scholarships.sk/">Study in Slovakia </a></li>
<li><a href="http://www.daad.de/en/">DAAD</a> - grants for studying in Germany.</li>
<li>just google!</li>
</ul>
<br />
<h2>
Epilogue</h2>
At the end I got offers from 3 programs and decided to enroll into the IT4BI Master Program. Now (at the moment of writing) I have almost finished its 3rd semester, which is hosted by a very nice university - TU Berlin. You can read more about IT4BI or TU Berlin on this blog: for example, in the post <a href="http://www.itshared.org/2014/12/it4bi-1st-year-business-intelligence.html">IT4BI First Year: Business Intelligence Fundamentals</a> we describe the first year of the program, and in the post <a href="http://www.itshared.org/2015/01/it4bi-distributed-and-large-scale.html">IT4BI: Distributed and Large-Scale Business Intelligence</a> we describe the specialization at TU Berlin. I also describe the process of applying to IT4BI in more details in the post <a href="http://www.itshared.org/2015/01/it4bi-how-to.html">IT4BI: How To</a> and you can also check <a href="http://www.itshared.org/2015/01/it4bi-faq.html">IT4BI FAQ</a>.<br />
<br />
I hope this information was useful. Stay tuned! </div>
Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com0tag:blogger.com,1999:blog-7332477090953898587.post-29347554314060516772015-01-07T22:59:00.008+01:002015-07-12T13:06:52.572+02:00IT4BI: Distributed and Large-Scale Business Intelligence<div dir="ltr" style="text-align: left;" trbidi="on">
<center><img src="http://habrastorage.org/files/04d/2dc/d0c/04d2dcd0c905451c8f6a978be58548dd.png" />
<img src="http://upload.wikimedia.org/wikipedia/commons/thumb/3/30/TU-Berlin-Logo.svg/200px-TU-Berlin-Logo.svg.png" />
</center>
<br/>
The first semester of the second year is devoted to specialization, and there are 3 possible choices in the IT4BI program. One of them is "Distributed and Large-Scale Business Intelligence" and it is delivered by the <a href="https://www.dima.tu-berlin.de/">DIMA group</a> at the <a href="http://www.tu-berlin.de/">Technical University of Berlin</a>. It is the specialization of my choice, so I'm happy to share my experience about it in this post.
<br />
<br />
<a name='more'></a>This is the second post about the IT4BI curriculum, if you haven't read the first one yet, go here: <a href="http://www.itshared.org/2014/12/it4bi-1st-year-business-intelligence.html">IT4BI First Year: Business Intelligence Fundamentals</a>.
<br />
<br />
I'll present the courses in the same way as in the previous post: first, I'll copy the description from <a href="http://it4bi.univ-tours.fr/home/master-programme/course-content/">the IT4BI website</a>, then I'll list things we study, and then add a line or two about projects and exams.
<br />
<br />
<u>Disclaimer</u>: I am a part of the 2nd generation of IT4BI, so it applies to the academic year of 2014/2015, and might not be accurate for later years.
<br />
<br />
<h2>
TUB, 3rd semester</h2>
So the following classes are offered at TU Berlin:
<br />
<ul>
<li>Big Data Analytics Seminar</li>
<li>Implementation of a Database Engine</li>
<li>Heterogeneous and Distributed Information Systems</li>
<li>Large Scale Data Analysis and Data Mining</li>
<li>Big Data Analytics Projects</li>
<li>Humanities: Interdisciplinary Communication</li>
<li>Machine Learning (Optional)</li>
</ul>
<br />
At TU Berlin IT4BI students can choose between <i>Heterogeneous and Distributed Information Systems</i> or <i>Large Scale Data Analysis and Data Mining</i>. Additionally, it's possible to attend <i>Machine Learning</i> classes, but without credit.
<br />
<br />
<h3>
Big Data Analytics Seminar</h3>
<b>Description</b>: Participants of this seminar will acquire knowledge about recent research results and trends in the analysis of web-scale data. Through the work in this seminar, students will learn the comprehensive preparation and presentation of a research topic in this field. In order to achieve this, students will get to read and categorize a scientific paper, conduct background literature research and present as well as discuss their findings.
<br />
<br />
Each of us is assigned a paper and a mentor, we have to work through the paper, analyze its upsides and downsides, and then we have to deliver two presentations about it as well as a written report. <br />
<br />
Some of the papers from 2014:
<br />
<ul>
<li>Summingbird: A Framework for Integrating Batch and Online MapReduce Computations <a href="http://www.vldb.org/pvldb/vol7/p1441-boykin.pdf">(pdf)</a></li>
<li>SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets <a href="http://www.dbis.informatik.hu-berlin.de/fileadmin/lectures/WS2010_11/Neue_Entwicklungen/ScopeMSR.pdf">(pdf)</a></li>
<li>SQL-on-Hadoop: Full Circle Back to Shared-Nothing Database Architectures <a href="http://pages.cs.wisc.edu/%7Efloratou/SQLOnHadoop.pdf">(pdf)</a></li>
<li>Fast Personalized PageRank on MapReduce <a href="http://131.107.65.14/pubs/145447/mod113-bahmani.pdf">(pdf)</a></li>
<li><a href="https://docs.google.com/document/d/1TFGkSPkTKaWIjLjCpDR2DTm6bmABDcXTeSejC-BAyDU/pub">Full list</a></li>
</ul>
<br />
Some of the papers from 2013:
<br />
<ul>
<li>Upper and lower bounds on the cost of a map-reduce computation <a href="http://arxiv.org/pdf/1206.4377.pdf">(pdf)</a></li>
<li>Making queries tractable on big data with preprocessing <a href="http://homepages.inf.ed.ac.uk/wenfei/papers/vldb13-BD-tractable.pdf">(pdf)</a></li>
<li>Jet: An Embedded DSL for High Performance Big Data Processing <a href="http://infoscience.epfl.ch/record/181673/files/paper.pdf">(pdf)</a></li>
<li>Only Aggressive Elephants are Fast Elephants <a href="http://people.csail.mit.edu/alekh/papers/HAIL.pdf">(pdf)</a></li>
<li><a href="https://docs.google.com/document/d/1TFGkSPkTKaWIjLjCpDR2DTm6bmABDcXTeSejC-BAyDU/pub">Full list</a></li>
</ul>
<br />
Note that papers were quite different in 2013 and 2014, so there's a high chance that the list will change in the next years. But still, it should roughly give you the idea what kind of papers are studied during this course.
<br />
<br />
There is no exam for this course and no separate course project, just a presentation about the paper at the end of the course. <br />
<br />
Link: <a href="http://www.dima.tu-berlin.de/menue/teaching/masterstudium/imsem/">http://www.dima.tu-berlin.de/menue/teaching/masterstudium/imsem/</a> (in German)
<br />
<br />
<h3>
Implementation of a Database Engine</h3>
<b>Description</b>: In this lab course students will learn how to implement components of a query processor with focus on complex queries as they occur in data warehouses and OLAP. Students will create a working SQL query processor that can answer a set of basic analytical queries.
<br />
<br />
This course is about implementing things we learned during our Database System Architecture classes at ULB on our first semester (you can read more about it <a href="http://www.itshared.org/2014/12/it4bi-1st-year-business-intelligence.html">here</a>).
<br />
<br />
The things we have to implement:
<br />
<ul>
<li>Basic IO</li>
<li>ARC Cache</li>
<li>Buffer Pool manager</li>
<li>Indexing, B-Tree</li>
<li>Operators (Table scan, Index scan; Filter, Nested loop join, Merge sort join, Group by)</li>
<li>Optimizers (Join order, access path selector)</li>
<li>Map-Reduce</li>
</ul>
<br />
Almost every week we get a new assignment, implement it in Java and submit it to an automated test system that gives you up to 10 points. We don't have to implement everything from scratch, they provide us with some backbone code, so we need to implement certain interfaces and call certain API methods. Yet it is quite challenging and quite time-consuming.
<br />
<br />
There's no exam for this course, and the entire course is a course project.
<br />
<br />
Link: <a href="http://www.dima.tu-berlin.de/menue/teaching/masterstudium/idb-pra/">http://www.dima.tu-berlin.de/menue/teaching/masterstudium/idb-pra/</a>
<br />
<br />
<h3>
Heterogeneous and Distributed Information Systems</h3>
<b>Description</b>: In this course students will gain conceptual, methodological and practical knowledge about the development and integration of modern distributed, heterogeneous information systems based on the concepts of model integration, data integration, promotion of information systems and metadata management for Business Intelligence.
<br />
<br />
<u>Note</u>: I selected the Large Scale Data Analysis course instead of this, so the information might not be 100% accurate.<br />
<br />
The topics covered in the class:
<br />
<ul>
<li>Foundations/Terminology of HDIS (FDBS, FIS, MBIS)</li>
<li>Dimensions of HDIS: Distribution, Heterogeneity, Autonomy </li>
<li>Heterogeneous Data Models in HDIS: structured, semistructured, unstructured</li>
<li>Distributed Data Organisation and Software Architectures of HDIS (FIS, P2P, CS, ...)</li>
<li>Interoperability and Middleware Platforms for HDIS</li>
<li>Persistency Services</li>
<li>Metadata Standards and Management in HDIS</li>
<li>Model-based Development of HDIS</li>
<li>Applications from Industry and Public Services</li>
</ul>
<br />
The class is given in a seminar way: students are assigned different topics which they learn themselves and then present to others. The topics that nobody choses are presented by the teacher. There is is a course project, and this semester it was about designing a web-based system for choosing a movie while also considering the best way to reach the cinema theater as well as suggesting a good restaurant nearby to go afterwards. There is no exam at the end.
<br />
<br />
Link: <a href="http://www.dima.tu-berlin.de/menue/teaching/masterstudium/aim-1/">http://www.dima.tu-berlin.de/menue/teaching/masterstudium/aim-1/</a> (in German)
<br />
<br />
<h3>
Large Scale Data Analysis and Data-Mining</h3>
<b>Description</b>: The focus of this module is to get familiar with different parallel processing platforms and paradigms and to understand their feasibility for different kinds of data mining problems. For that students will learn how to adapt popular data mining and standard machine learning algorithms such as: Naive Bayes, K-Means clustering, PageRank, Alternate Least Squares, or other methods of text mining, graph analysis, or recommender systems to scalable processing paradigms. Students will subsequently gain practical experience in how to analyse Big Data on parallel processing platforms such as the Apache Big Data Stack (Hadoop, Giraph, etc.), the Berkeley Big Data Analytics Stack (e.g., Spark) and Apache Flink.
<br />
<br />
Topics covered:
<br />
<ul>
<li>Hadoop MapReduce</li>
<li>Joins in MapReduce</li>
<li>Apache Spark, Apache Flink</li>
<li>Math refresher: Linear Algebra, some Probability and Statistics</li>
<li>Classification: Naive Bayes, Ensembles, Logistic Regression, SVM</li>
<li>Stochastic Gradient Descent</li>
<li>Clustering: K-Means in MapReduce, BFR, Cure</li>
<li>Recommender Systems </li>
<li>Dimensionality Reduction, SVD</li>
<li>Graph Mining, PageRank, Pregel</li>
<li>Large Scale Statistical Natural Language Processing</li>
<li>Online Learning / Stream Processing</li>
<li>Privacy and Legal Issues</li>
<li>Visualization Analytics</li>
</ul>
<br />
All topics are discussed in the context of processing data on a large scale. This course is not a replacement for Data Mining course that we had at UFRT during our second semester, and it's different from the Machine Learning classes at TUB - because this course deals with scalable algorithms that aren't covered in other IT4BI courses.
<br />
<br />
We had 4 assignments during the course:
<br />
<ul>
<li>Hadoop assignment</li>
<li>Apache Flink assignment</li>
<li>Math refresher, implementing matrix multiplication in Flink</li>
<li>implementing Naive Bayes in Flink for text classification </li>
</ul>
We also have a project on any related topic of our choice that we have to defend by presenting a poster about it, and there's also an oral exam at the end. The exam was about the project and about some topics we studied.
<br />
<br />
Link: <a href="http://www.dima.tu-berlin.de/menue/teaching/masterstudium/aim-3/">http://www.dima.tu-berlin.de/menue/teaching/masterstudium/aim-3/</a> (German)
<br />
Github: <a href="https://github.com/sscdotopen/aim3">https://github.com/sscdotopen/aim3</a><br />
<br />
<h3>
Big Data Analytics Projects</h3>
<b>Description</b>: In this course students will learn to systematically analyze a current issue in the information management area and to develop and implement a problem-oriented solution as part of a team.
<br />
<br />
During this course we had to choose a paper in Graph processing and implement it in groups in <a href="http://flink.apache.org/">Apache Flink</a> using the Scala programming language.
<br />
<br />
We could select papers on the following topics:
<br />
<ul>
<li>Structural diversity measured through number of (strongly or weakly) connected components</li>
<li>User engagement measured through k-core decomposition</li>
<li>Importance of a node measured through centrality</li>
<li>Spatio-Temporal Dynamics of Online Memes</li>
<li>Social Contagion</li>
<li>Minimum Spanning Forest and Single Source Shortest Path</li>
<li>Graph Coloring</li>
<li>Approximate Maximum Weight Matching</li>
</ul>
<br />
We meet each week and discuss problems that we encounter during implementation of the algorithm. Everything is organized through github: we create issues, send pull-requests with changes, etc.
<br />
<br />
<a href="https://docs.google.com/document/d/1iBCAGJbPq_x29QTrfjq3KrMEwwg7CLRBPNejqEgkOK4/pub">Here</a> you can find links to the papers that we had to read in order to implement the algorithms. There's no exam at the end and the entire course is a big project, so it mostly involved coding (and working with git).
<br />
<br />
Link: <a href="http://www.dima.tu-berlin.de/menue/teaching/masterstudium/impro-3/">http://www.dima.tu-berlin.de/menue/teaching/masterstudium/impro-3/</a> (in German)
<br />
<br />
<h3>
Humanities: Interdisciplinary Communication</h3>
<b>Description</b>: In this course students will learn to work on their authentic presentation and what kind of possibilities of intervention they have working in groups (knowledge about role identification and group dynamics). This workshop brings into focus multi-cultural aspects of communication.
<br />
<br />
The humanities component of the 3rd semester is quite different at TUB since it's not an language class. This course is held during weekends, and there are 3 weekends in total: one in November, one in December and one in January. At the end there's an oral exam.
<br />
<br />
Link: <a href="http://www.hi-gh.eu/index.php?page=inter-komm&hl=en_GB">http://www.hi-gh.eu/index.php?page=inter-komm&hl=en_GB</a><br />
<br />
<h3>
Machine Learning</h3>
<b>Description</b>: In the lecture, introductory topics in the field of machine learning are presented. After the lecture, the learnt methods are revisited and exercises from the previous week are explained in the exercise session.
<br />
<br />
Note that the description is not from the IT4BI page - since officially it's not the part of the IT4BI program at TU Berlin. But still they offer this course to let us dive deeper into the machine learning topic.
<br />
<br />
The teacher of this course is Prof. <a href="http://www.ml.tu-berlin.de/menue/members/klaus-robert_mueller/">Klaus-Robert Muller</a> - the head of the Machine Learning department at TU Berlin. He is quite famous in the Machine Learning world, as his group was one of the pioneers in the Kernel Theory.
<br />
<br />
So, here's the content of the class:
<br />
<ul>
<li>Bayes Decision Theory</li>
<li>Maximum Likelihood Estimation and Bayes Learning</li>
<li>Principal Component Analysis</li>
<li>Independent Component Analysis</li>
<li>Fisher Discriminant Analysis</li>
<li>Clustering, k-means</li>
<li>Expectation Maximization</li>
<li>k-nearest Neighbor</li>
<li>Model Selection</li>
<li>Learning Theory and Kernel Methods</li>
<li>Support Vector Machines</li>
<li>Kernel Ridge Regression and Gaussian Processes</li>
<li>Neural Networks </li>
<li>Boltzmann Machines</li>
</ul>
<br />
There are no projects and since we cannot take this course for credits, there is no point in taking the exam (and probably it's not possible either). However, every week there is a homework assignment to reinforce learned things. The course is quite challenging, but if you're motivated, it is certainly worth the effort.
<br />
<br />
Link: <a href="https://wiki.ml.tu-berlin.de/wiki/Main/WS14_MaschinellesLernen1">https://wiki.ml.tu-berlin.de/wiki/Main/WS14_MaschinellesLernen1</a><br />
<br />
There's also a second part of the course in the second semester that studies more advanced things in Machine Leaning:
<br />
<ul>
<li>Locally Linear Embedding</li>
<li>t-Distributed Stochastic Neighbor Embedding</li>
<li>Stationary Subspace Analysis</li>
<li>Dictionary Learning and Autoencoders</li>
<li>Canonical Correlation Analysis</li>
<li>Relevant Dimensionality Estimates</li>
<li>Hidden Markov Models</li>
<li>Kernel Methods for Structured Data</li>
<li>Bioinformatics, Structured Output, MKL</li>
<li>One-Class SVMs</li>
<li>Neural Networks for Structured Data</li>
</ul>
<br />
The second part reinforces the first part and dives deeper into Unsupervised Learning: the first half of the course is devoted to a broad set of Dimensionality Reduction techniques.
<br />
<br />
<h3>
Database Internals & Scalable Data Processing</h3>
Lastly, the students of our generation had an opportunity to attend the lectures on Database internals by Prof. Volker Markl, the head of the DIMA group. But nobody actually chose to take it since it's mostly a repetition of the Database Systems Architecture class that we have at ULB. Still, it might be useful to know that it's also offered - for those who would like to refresh their knowledge on the database internals.
<br />
<br />
Link: <a href="http://www.dima.tu-berlin.de/menue/studium_und_lehre/masterstudium/idb/">http://www.dima.tu-berlin.de/menue/studium_und_lehre/masterstudium/idb/</a>
</div>
Anonymoushttp://www.blogger.com/profile/03991393318576196455noreply@blogger.com7tag:blogger.com,1999:blog-7332477090953898587.post-64305729310929005052014-12-31T03:19:00.000+01:002015-01-24T07:24:29.471+01:00A Round-Trip to RDFThis is my first post in the blog and I would like to start off by <b>sharing</b> an <b>application</b> I developed as part of my studies at <i>École Centrale Paris</i>. Its name is <b><i>RDF Usine</i></b> and it is meant to <b>convert</b> plain text files (e.g. CSV) into <b>RDF</b> format.<br />
<br />
I have published not only the <a href="https://rdf-usine.googlecode.com/svn/trunk/executable/rdf-usine.jar">executable library</a> but also the complete <a href="https://code.google.com/p/rdf-usine">source code</a>. <br />
<br />
<a name='more'></a>Without any further introductions, let's see how the application looks like:<br />
<p align="center"><a href="https://www.blogger.com/blogger.g?blogID=7332477090953898587" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://www.blogger.com/blogger.g?blogID=7332477090953898587" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="https://www.blogger.com/blogger.g?blogID=7332477090953898587" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><img alt="RDF Usine" border="0" 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" width="550" /> </p><br />
<h3>Application Interface</h3><br />
As it is illustrated in the figure depicted before, the interface is made up by <b>three main components</b>. Each one of them is briefly described as follows:<br />
<ul><li>The "<i>Configuration Pane</i>" is on the left. We will use it in order to define the different <b>parameters</b> that are inherent to each file format.</li>
<li>The "<i>Input File Preview Pane</i>" is on the top right. It shows a raw <b>preview</b> of the input file.</li>
<li>The "<i>Output RDF Files Preview Pane</i>" is on the bottom left. This tab shows a <b>tabular representation</b> of the input file (in "Table View" tab) and a preview of how <b>RDF output</b> files will look like (in the tabs "Turtle" and "N-TRIPLE").</li>
</ul><br />
<h3>Main Features</h3><br />
The key features of <i>RDF Usine</i> are enumerated as follows:<br />
<ul><li>The application is <b>multilingual</b>! It supports <b>English</b>, <b>French</b> and <b>Spanish</b>. You can easily change the language by making use of the "Language" menu:<br />
<br />
<img alt="Language Menu" height="100" 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" /><br />
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If you speak <b>other languages</b> and would like to contribute with their translations, your collaboration will be pretty much appreciated! Please contact me for more details.</li>
<table><tbody><tr><td><li><i>RDF Usine</i> was developed using <b><i>JavaFX</i></b> technology so it could be run not only in <i>Windows</i> but also in <i>Linux</i>, <i>Mac OS X</i> and other operating systems.</li></td><td><img alt="Java" height="45" src="data:image/png;base64,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" /></td></tr></tbody></table><li>RDS Usine also "speaks" two <b>RDF formats</b>. Information can be exported in:<br />
<table><tbody><tr><td><ul><li>Turtle</li><li>N-Triple</li></ul></td>
<td><img alt="Turtle" 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" width="45" /></td> <td><img width="45" alt="N-Triples" 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" /></td> </tr></tbody></table></li>
<li>The range of accepted <b>file encodings</b> is quite broad, including UTF-8, ISO-8859 and US-ASCII, among others.</li>
<li><b>Multi-line</b> fields are supported. For example:<p align="center"><img alt="Multi-line fields example" height="200" src="data:image/png;base64,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" title="Multi-line fields example" /> </p></li>
<table><tr><td><li>It is possible to <b>preview</b> the <b>complete</b> input and output files. According to the user needs, it is also possible to restrict the visualisation to the first <b>10</b>, <b>100</b> or <b>1000</b> rows (for big files, it is advisable to use one of these filters). You may use the "Preview" menu for doing so:</li></td><td><img alt="Preview" title="Preview" height="50" src="data:image/png;base64,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" /></td></tr></table><p align="center"><img alt="Preview Menu" height="120" src="data:image/png;base64,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" /></p><table><tr><td><li>Configurations can be <b>Saved</b> and <b>Loaded</b> to be applied again afterwards. This includes all settings specified in the "General settings", "Fields" and "Prefixes" tabs:</td><td><img alt="Save/Load Configuration" title="Save/Load Configuration" height="50" src="data:image/png;base64,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" /></td></tr></table><p align="center"><img alt="Configuration Menu" src="data:image/png;base64,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" width="100" /></p></li><li>Possible field <b>delimiters</b>:<table><tr><td><ul><li>Semicolon: ;</li><li>Pipe: |</li><li>Comma: ,</li><li>Space</li><li>Tabulation</li><li>Dollar Sign: $</li></ul></td> <td><p align="center"><img alt="Field delimiters" title="Field delimiters" height="150" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJoAAAD+CAIAAABN+IsMAAAQZElEQVR4nO2d21MbV5rA9Q/5YXsetio1L7NTu6naTGKUZJLicSqpTB4C9uxkJtnZXW12a2tD7FTK8zIVB7AMBgTYxjYGCXO1AGMjdL/f1UISSOJikFR57XmQLPU5ffomCaQ++n71PaDur7uP+qdzupvS0aerABSh63QDgHaC6CyVK69L5ZNS+eQcQjtRKp+Vy2W+znKlcnRe3j85DxfPXIev93KnEFoJT/517Ogse1p6XSrXdB6dl735M2Mw//lm+uqzxDtLEJqJq88Sv7eys5FC/PhMV6lUzssV9qT0vevgnaXEx2vs17aD/3PlIbQSAzvZXy0mfvectSSPdZVK5eS87Dh4/cFK8oNVdjJStO0XfdmiH0IL4c0Wt9jiTdfB25bED+5DXXWkXU+f/Hox/hdbzr5fDKQyUXY/BqGFiLD7nlRmO32kX0599TKjq1QqxfPyCnv8T4vx/3Uc+PbzMTa9D2iHQDLtyx19vMZef7GP6rTnfPuHMTYNaIhgMu3LFj9eTeE6/2cv59s/iKZYQEMEEqw3W/xIqPObvaw3nYsmwaeWCCRSnkzho9UkQaeHzUUSKUBD+OMp937+oxWhTlvGk8qGE0lAQ/hiSbLO/7Zl3KlsON7pBgJq8MUSrvThbwk6d/fdyUw4ngA0hDeacLIHv11O4DoNr/Zdif1QLA5oCG8k7kjlPhTq/K9XaWc8HYwq2830dQbl+vTFthsg4onE7Mnsh8txgc6XrCPOBqMxOUzXGYZhrpt4i6zfv4+8Bi4LdzhmT2Q+fEbUGWMDkagkz2/qGebalHQScGm4Q9G9ROYDoc7/fMnaoyl/WGpjkNltuEIRW3yfpHMntRdN+sIRcTZu6hn9zQ2JjHpag2tT/HXVPfAyrk2hWyD731CXjh1ZQVM7z+bm5u3bt4fEGRsbk9jcGYzYYukPlmK4zv94kbJFkr5QWJSNG3qGuTYpnvAmh5c0eY1hGP2NjTevb+gZ3oLqWr2+vsHkNWTrDXXpk9d4R8LWdSu7u7s//vjjJ5988j6JL774YmJiQmJzZyC8G2PfF+r8y3ZyN5zwBkOirN/QM8zghHhCKDQxKMhAt7qhZxj9jXVkJX8LNHtdXbqwubxtuxebzXb79u1PP/1U6HJyctLpdEps6/CHXkZS71uiuM5/3068CsU9AfFN5XUSbGKb4ad4YhDxRdCpIr2e0kD609cdhMNhu93+008/8Y0ODAyYTCaXyyW9rd0f2gkn9QSdW/GXwZgnEBRnYpBh9N+tia5f+07PMIMTUku/06N7wHeJ7mJNVfrad/qqQanWdCfhcNjhcIyMjFSNDgwMTE9PV11Kb2j3BXdCCb05guv8eiv+Mhh1+wMS3BtkmL6hVcn1g/ewhatDfY2lQ33oDvA9IsmBVRXp6JakBd1NOBx2uVyjo6PffPPN7Oysx+MJBoOyW+35Ai/IOjfjO4GoS1Jn9RSJCyWdQXTZ5elcHepjtKQzEAhEIhGv17u3t+fz+UKhkJJNbL7Ai1C8T6jzq83Yjj/i8vllGB9gGIYZGMeW1RasDPUha8cH0OShPqZvaAXdG3/BylAfL39FTTpyqGo78HbSh83r3w7G+8xhgU5r9IU/4vT6ffIsf9uH/ct2YLyxtiq8Rt+3y/wtv+1Dl4wPoCnL3/bxdrasPr1xWHQdpex6/FuBWN+iQOefrdFtX9jp7XQDATW88vg2/dGrBJ3PI1vekMPjBTTEK7fX6otcXQjhOv+0EdnyBO1urwfQDi9d3ufe8HskneFNd9Du7nQDATXsuDxknV9uhK3uwJ7LDWiIHad7wxN6byEo0Lkesrr8NqfbBWiHFw73ujv43lOBzj+uh547fTZnpxsIqGHb4VpzBd4l6FwLbji9uw4noCG27a41V+Dd+QCu899Wg+sO7yu70wFoh+0956rT/xuhzj+sBlbtnpf2TjcQUIPV5lx2+H4z76/pPDovb2ZO/sUc//yZ/6nNu25zbtkcL/YgNBCbNseyzTXzyverOf+XOxldpVI5KZX9hbNPnrP/Oh/4YdM3b/Mu73lWILQQz/Y8D3a9X674/uF+4P8dOV2lUimVy+mTc2Mg//Zi9B/v+95+5H3nMYRm4p/nvL98FPzdRmohcfTmh2jOSnvx9PzG9hKgQWbXX1jC+/GjM125XC6Xy6Vy2bq5yQGaZWd393Wp/EZnqbS0tNTpJgHNs7S0VCqVdKVSqVQqnZ+fg05Ns7S0dH5+rqu6PDs7s1gsnW4S0DwWi+Xs7ExXdXl6ego6NY3FYjk9PdVVXR4fH4NOTWOxWI6Pj3VVl8ViEXRqGovFUiwWdScnJ0dHR4VCAXRqGovFUigUdMfHx4VC4fDwEHRqGovFcnh4qCsWi/l8PpfLgU5NY7FYcrmcrto1M5mM2WzudJNaxdjPMAZrp1vRGcxmcyaT0RUKhYODg3Q6La7TakC+7d5vZNvdFrY9InpcZzqd1hUKhVwux7IsWafVwKCnyGoAnd2I2WxmWVaXz+ez2WwqlSLpbNNpviy01dr2YjabU6mUtE6r4ULG1osCdOry+Xwmk0kmk8TBVjDWkjNq8BJZYz/Tb2RZYz+ykvea/zEhjAK8TGTPYss5jqBTJrnfyPLbr+lPgtlsTiaTMjrruoh9lDX289Yg6msn8s1Kq4FhmP7+/vp69HOC66ym1w9pNVTXVXfayMPSUJ1KkvEGa2gkwqnprD6liOvkOF4PRN+uYCjmLcDPDn5yUYOoTrEh3moQ9CB0Q/4LRclirdcguM7FxUXpDWpSkbNOGCAbHUnyZInrVGET25L3t6Jk9Cgau+/DWFxcVKeT45AxEr0u4Veo5nWKnVXychGdypJ7Xif/PRM7AD+vhd5J2i/0Tima18m/PIq9/RZ0qumeotdORcm9ptNqQN8f9tyC3yzWb0Fb0km6KZW4s+U1UP7OFk3uQZ0oglsU9AraWN2STsGh8WcYkeYI9iKTTJnOK1euNHQuLCx0uklA8ywsLCC9E3RqGtBJFQsLCzDY0sPCwsI09E5qgMGWKkAnVYBOqsBvhZ4+fdrpJgHN8/TpU6R3gk5NAzqpAnRSBeikCtBJFaCTKnCd8/PznW4S0Dzz8/Ogkx5AJ1WATqoAnVQBOqkCdFKFMp2SX83U9lcZ6QLX+eTJE0IW9vVY9LvIoLN7ePLkiXqdoLBbaVKn8PvtQDfQBp2CiQnY7HRhL5ZcCbRAGwZboU7edHnBnB+JqfdAyzSlU+InDUg/LlDdmrgnrU9e7zaU6xSdQkYcbPlUE+o7Ep16D7QMrvPx48eELMkupEKn9NR7oGUeP358KTprg63k1HugZS5Cp8QEWBhaL5YL0Yn+uBMjvI0iT70HWuaCBlvJJ0uxqfdAyyjTqRz4b1FHAZ1Uget89OhRS/sDnR3l0aNHoJMe2q0T6CigkypAJ1WATqoAnVSB65ybm+t0k4DmmZubA530ADqpYm5uDvkhGtCpaaB3UgXopArQSRWgkypwnQ8fPux0k4DmefjwIeikB9BJFaCTKkAnVajUKZhVD9+R7SqU66yaxKsQg86uAtf54MEDUhpMRtAGDx48kNcJMrWCEp0Kv2wpXwBeYYV6lQXtYZZEAwU6lfRNZQXgFVaoV1nQnrMaRGeG9xrt0am2ALx0hXqVBe0Jze3ZDtoWnepLTEuWZVVZxbWRAs9PSq6dcr9G0UQB+HbqrA3N8CNknMI7WzmfneydAnugk6fz/v37hCzSz8vgq1UVgL84nYLrbE9x//59BTo54j+FeCXr1RaAb+NgixzqzSML6JTRyXGk35HhnzY1BeDbeiuEPo/29GCrSifQ7YBOqgCdVAE6qQLXOTs72+kmAc0zOzsLOukBdFIF6KQK0EkVoJMqQCdV4DpnZmY63SSgeWZmZkAnPYBOqgCdVAE6qQJ0UgXopArQSRW4zunp6U43CWie6elp0EkPoJMqlOvE5tH37KyerkaZTsEsO2QOHtA1KNHZ019E1hZKdEI9Y82A6zSZTMIk6RnNcrXoZaa6Y3PwsaP2+HxNtZhMJnmd9VNL7KMyteglp7pjk+6tBgNvH1C/XjUmkymZ3JLVyXG83iKcTkSuRU8anpHZRKJpUL++SUwm09bNq4p0VqlJlZjsxxFunohT3SVtQv36ZlCtk+PwwU9ap/hUd1FFUL++aUwmU3J6UKVO6YnyHG9IlZzqLlqmHurXN4vCWyEM5AooVYtecqq7dPcEn02gRGdjznz9Nf9sS9ail57qLpyDbyDso7YG9MqD65yamhLkCH71FO2LMrXo5aa6I3sXdGTRx1WAxNTUlKxOGXr5V7a6DdBJFaCTKkAnVbRBJ9A9gE6qwHVOTk52uklA80xOToJOegCdVAE6qQJ0UgVBp+5vuxAaDbLOXzwMQGgupHR2/IMGoSrIOicmJnSgU4NRVTYxMQE6aQjQSVWATqoCdFIVoJOqIOu8d++eDnRqMKrK7t27d4E6b51yMbu342+1F0Kpzs8SYv8gPPlM7hig89Kiid4ZM3Pq9IDOSwvQSVWATqqibTrfsv/Mu6D+fGumsaqmc/mksT4R6/g7pzLapTNmPs2+9eblZwnkFunWKRc7/bmhcCYbA6OXqXN8fFzXymA7k43xOuitU47jydb9bVe3fIL1YIi2RFXZ+Ph4O3Tyh1OOMy83dOL5M9kYLwGiXdEmndXxsz7AorZA56VFW3R6b52i10JFOmGw1YjO6l0uXyd27XzL/rOSfydBqI32DLbIrWxt4EV18jdZPuFUPrlCKIx23Qp5q844juNOs2+RBlv+f33hqnmpOsfGxsR1QnRvVJWNjY1dvbkFOjUfdZ2DoJOCqOuEwZaGAJ1UBeikKuo6rwxOg07NR0Pn1ZugU/NB7p13797VgU4NRlXZ3bt3kWtnXSeE5kJUJ4RGg6CzyWn5QBcAOqkCdFIF6KQKXKfRaOx0k4DmMRqNoJMeQCdVgE6qAJ1UATqp4hJ1ohVxoC7VRaBMp6DOUTP1+i5XZ2/WMcN13rlzh5AlrLSJ1XFUAui8eO7cudOUTo7DKxrLAoPtxdOKTpU1jEHnxdOaTuEKrM68oFSgiE7RcoC1SqEsXlWSD7LxmxTpUoQGK9LyWqEmXoZGP2kt6kROmrAaJ2JAQqd49Xq2WvdefAhA98oaDUSd2HWh5g2pKIvXGdVkIa426iSUOBbaVjLY8mv3yp5ZscLKyNFEaqOLFwhWdRXpInCdo6OjhCwlOonnlX9S5XQSq9fL3qBWhwSR+zTeBw3PIA225K21xOjoaCs6Gw7JJ0CZTvHq9WiVbTHqHwTsoivVNGp1zszMNKuT3yOb7p2S1esV6eS1hnS03tK5s7PTlE7sskY6A4qunbLV69U92tayxT9I9XdEp85YLKZaJ/GKRbyzxR5GSIOtVPV6WZ38kvXiCrGbbMKdLTU6WZZVohNF7BQjmVKnSPgcUd9G1WBLvIESHA1LNFjpHWyz2aycTgoRe8DRNqOjo4eHh72nU6sPljKMjo7m8/mGzpGRkU436QJgjf2CW2cKZXLcyMhID+gUXPzpG2ar9IjOXgF0UgXopArQSRWgkypGRkbyj//Y0Dk8PNzpJgHNMzw8fOWKHnRSwvDwsP6v9r8DCxdGRSppcNsAAAAASUVORK5CYII=" /> </p></td> </tr></table></li><li><b>Escape characters</b> may also be configured:<br />
<p align="center"><img alt="Escape char." title="Escape char." height="30" src="data:image/png;base64,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" /></p></li><li><b>Headers</b> can be:<br />
<ul><li>Read from a user-defined row number of the file.</li>
<li>Defined in a customised way -in tab "2) Fields"-.</li>
<p align="center"><img alt="Headers" title="Headers" height="60" src="data:image/png;base64,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" /> </p></ul></li><li><b>Entity classes</b> can be defined as free text.<br />
<p align="center"><img alt="Entity type" title="Entity type" height="30" src="data:image/png;base64,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" /></p></li><li>You need to specify where to <b>start</b> and <b>end</b> processing the input files? <br />
That is quite easy with the file "Boundaries" options!<br />
<p align="center"><img alt="Boundaries" title="Boundaries" height="60" src="data:image/png;base64,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" /></p></li><li><b>Subjects</b> can optionally have a text prefix and they may be:<br />
<ul><li>Read from a user-defined column number of the input file.</li>
<li>Defined as auto numeric values.</li>
<p align="center"><img alt="Subjects" title="Subjects" height="90" src="data:image/png;base64,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" /></p></ul></li><li>Need to work on <b>multiple files</b> or <b>entire folders</b>?<br />
That is not a problem! Use the <img alt="File(s)..." height="22" src="data:image/png;base64,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" title="File(s)..." /> button to select one or more input files or the <img alt="Folder..." height="22" src="data:image/png;base64,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" title="Folder..." /> button to select a directory and look for input files recursively in all the subdirectories.</li></ul><br />
<h3>Preview and Export</h3><ul><li>While working with multiple files, you can use the "<b>Preview</b>" combo box situated on the "General Settings" tab to choose the file that you would like to preview in the "Input File Preview Pane" and the "Output RDF Files Preview Pane":<br />
<p align="center"><img alt="File selection" title="File selection" height="90" 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VGIsmlBmRYIhsvieMrtixCDtWyB5E6MYpOEUZd50vkb1JU3yeg/mS61Tebx5Vsu6xKpSJRqUTaf0t3iUmGph2lvB98PIrG1w3wa0a7BjWl3794NBoORSAROUhekk7hPyyo7iTcBdSDjRrHyMD2ZJlJW5SRfRjraEnVNpouFC9ejKptS2P4dTfWIIVhUr9QixwLlR37J8ETSSKXOlHXSjahP6kK5WiSJhSErj2MB0YGLtLS0lwzp8zdoXVqhUgSimUF1UjqdhpNUgchJqVSKOwgu76TlDuIEl3AwLa0gESmJIbIaJ1EKrG68rtY0SgtZVYZgWiBYwaO2qtwhMiO1TGdW7yTFWrbnJLk1vfKETKlX6dtaCIQ6QuSkeDzOfUQJTlIFCk4aHh7uUPx8UnEw5Uc06oBVHhOrcpJg9JTd7ZDKr6rFopcMlIMJ4kIqrgcKW0WWSRmaBYUoNVLcmVU7qUItVTip2gOEl+RP6wlqoZeJQKgkuDFteHgYTlIjO3FSxyXBngptbapWJwmEQS/wkniHqYyik8TritsOsSlt5zPFpxAO5SLDCfdgKnWmuHPEyfhuqVhL5f0kSZ8oC57fxpN0ApcF5yYQ6g44SdUoOOnOnTsdlb/HQTQYKThJsO3BhbKT5FaKZEZSxdj5x5IERQnbWVp1rDDtu6RwLFvSmcrn7pSnQdLNNqVzdzJrcZUmndzETjpdqyYvAtHE4Ma0O3fuwElqpC5OKg5/tPGRnOtQt1uUnCS77lTjsa46CukSfR7AreD5MuXHkZvkKW5oCVa9ZD/tJF45rFSL4ueT5M+qVDwzIlqj4/5QqP3PBQTi2QacpGpqdJJvXDhUiQ6eUc/dyRzDK+2gKDiJVmDxZ9F5vEvLxwIK2z913d6gr02V1tCKjyP/cdRy9gqdKXGS9NuDqqml3AmU73Egrghtx9VFNiYt6P9LMsf/qLtNCISaAk5SNQpOun37dgftM7MCpAOQ4Mvl6I7hGJ+stHZHKVB8yplHxjoy2y11+XwStZ3c4yjNzHiRVOhM6ffdiZ+xqlrkuoI+1SMvChpQXp4tI6667vpHIBoQ3Jh2+/ZtOEmN1OgkRBuEcMkUgdhlASepGgUn3bp1qwNOas/gpmg1fPkFAtE2wY1pt27dgpPUCJy0a4NYR4WcELso4CRVAychEIhdFXCSqlFw0s2bNzvgJAQC0V7BjWk3b96Ek9QInIRAIHZVwEmqBk5CIBC7KuAkVaPgpE8//bQDTkIgEO0V3Jj26aefwklqBE5CIBC7KuAkVaPgpKGhIe79QyAQiHaKjkvLQ0NDcJIaqegkBAKBaL+Ak1SKgpNu3LhB+6o4AABoeW7cuAEnqRE4CQCwC4GTVAqcBADYhcBJKkXBSYODg83+tQEAgIYwODgIJ6kROAkAsAuBk1SKgpMGBgaa/WsDAAANYWBgAE5SI3ASAGAXAiepFDgJALALgZNUioKT+vv7m/1rAwAADaG/vx9OUiNwEgBgFwInqRQ4CQCwC4GTVIqCk/r6+mTeTW23huRoT+iZ/jLVSKjnaF0byz09rRxtd+m6tpuaJNRzVKPp1gpLojRNdKMFOhmAFqOvrw9OUiM1O0nbrSGH1a0tbbfKh0ttd7m5oZ6jOx7eS8KQFlN2kvBn8n6pLYr9SMsMAKgjcJJKUXDS9evXJe+j6A/9FmTnT6Dt1miOdndT5CZQiaSiUM9R/m6FVsBJADSY69evw0lqpEYntf5gWScn9YQkEx1J7whfkpOkSv3Y+t0MgMqBk1SKgpOuXbsmfSMpI7EI4fYNkZRiA8nE4mhPqJi/PCTLFkjuulRtmZ0P93wJlOU3YdnEE5OTJL7tso2uoZH03hFc5RsrKrL1J70AbJtr167BSWqkVifxJqAOmdxQKNrEV1ivkjrp6FFhwaLDAvzekHBjqLIpifbtdAJCNFpUr3TcL02OBJMkos0yranWSfTeEXZ0qKebP3YhKBNKArsZOEml1O6krS1ihkIfgQlEU4VKTqIUWN2grTiKl2cN9VgQE1QlsJLMXORod/dR+cegtUt67k75mJ/0OsU1ouTimRsAuwo4SaUoOKm3t1f5TS2OnIJNEslYWDZRVU4S3FdUkrCkKv/o5+wkNxQL18HkihO2ijQptb1cLyk1TdyPVc6TZBPJPaSgj6AksLvp7e2Fk9TITpy0tSWYJ9C1UJuTqltcErmjtk2lHS9ZUXeN5PZsqq1PuApYjZOUy6XOwIgsOEUBdjlwkkrZqZPE45yCkyj3lZ0ktwilcKMauEq3mXmLNpyXrLQDJxWTyVYh045K5YomaXxbdtSBALQBcJJKUXDS1atXq3hnCZPQRl/yGnUTSMlJyhOlbY6pdZ8n8YUePUo7QVGDk3hTVuWk6goW9SonIygJ7HquXr0KJ6mRGp2k7Zbu+BBXqOfuZI7hSQ6e0XY4pAUSfhMdrqYPsqGeo+Lj2jscjunCKK0nVuekCv2ovFUk3IGT9o7kiyskHYB1O7DrgZNUioKTPvnkE8n7KDkQRv+CHdnb5E5Qt7bS2h2lQPHnnRQPpklT1ev77uTbWbWTlPqR8n13xJ6cdK4p7B1hbmrVUBLY7XzyySdwkhqp0UkAANAOwEkqBU4CAOxC4CSVouCknp6eZv/aAABAQ+jp6YGT1AicBADYhcBJKkXBSR9//HGzf20AAKAhfPzxx3CSGoGTAAC7EDhJpcBJAIBdyK520sip+ef2zD+3Z/65QwFHpcSOi0YucdeYTAomcJAr7VRs521TcNKVK1ea/WsDAAAN4cqVK9t0UnlAr99A/AxZv3CIaDycBAAAKmBbTuIHX3EYLzA1Dr1jjNIov20qFlu7P9TjpMuXLzf71wYAABrC5cuXa3QSISR+dObH6+f2MCNVD7uVR/ltUVWxYwyX5uDF9boVCycBAMDOqNlJ/JKdaGjmr9dzlN8W7eqkRCKBzycBANqboaEhzkmJRGJtba2ik2JdcvMhfkTm9makg77wStce8dJf15hwWC+lVyik2mKFCHaShKWJNsnIvFQnEemNF8ZoTiKeguw0orRil5KP3zUmcFImk1lbW+OdNDY2dvXq1T8DAEB7cf36db1eTzopk8koOklpKiDU1Q6dJIliOQ10Ukyal3xSiZPWpeWIsoiPgRBbbsRqZ7md5YunYpyTcrkc6aRoNMqy7OzsrE6n8/l8LpfL4XDYbDaLxWI2m00ljEajcfSDl/fuffvPRnlGP3h5796XPxgtvvzz2xXSAwDoDA8PP24Y/f39t2/fnpqa+kyZc9/du/fAu72ll73vHiBf9757YO/e756TzdD77oG9e/eWk4iyKzI9Pd3X11f9E82XWFhYWFxcXF5e1uv1JpPJZrMxDOP1elmWjUaj1TmJ/6t/x06ijfK0zarylR0UK/8g0hmS9ApXjrhYvitKZ/bKBuI6p5RA7LBTsYLcDpxknkR1klar1ev1fr/f7XYzDGO3261WK6elMuO/eHnfvmNXzNVz5ViNGQAAZrPZbB4ZGVloGAMDA3fu3Hn48OF9Zc59dx/JgZ9cI+9e+8mBffu+e06cgU917ScHRHkEtxWZmZnp7+/fxqMtLi4uLS3pdDqDwWA2m+12u9Pp9Pl8qnQSUX5prDdeYBrnpFLjyUPhwsaIihW0ipaeNkkqV8GXJrehJXVSMpmMxWKhUGhubs5oNAYCAY/H43Q6HQ6H3W632WxWq5WTk8VisUz88p/27fvBFUsNXPlBrTkAABaLZWRkZLFhDA4ODg8PV3bSf39v374D717jf+ZfcM5598C+fd/7b7kM96+9e0CYgZZD0Uk1PdTS0tLS0tLy8rJOp9Pr9Uaj0WKx2O12l8vl9/tDoVAsFksmk7Ws3e14P4kuj93gpD3MSBWNFDlpfX09mUyurq6Gw+HHjx+bzWaWZX0+H6clhmE4MxF88sN9+1751X17Ddz/1Su1ZgEA2MfHx3UNY2ho6N69ezMzM1PK/P6f9+372nt9xVd9732NfMm9/uffy2boe+9rZHJ6Dhm0Wu3g4OA2Hk2v1xsMBk5I3MKdx+MJBALhcHh1dTWZTK6vr1dyErGJIhpMxX/4y6uiRifxNdKnX1UVK+XZOYn+ma3qnbS5uZnNZjknccfB5+fnLRZLOBwOBoOcltxut8vlcgrp/dfnn3/111POGpj69au1ZgEA3L9/v3GbVbdu3RodHZ2bm5tW5vy/PP/81382IHj9/L+cJ1+WX0kyDPzs64Lc3BVRDhkeP348NDS0jUczmUxms5kTksPhcLlc3MIddxCcc1I2m93Z55P4MV00bSJkpqATovDSUC7attlesVLk95P4h1LeTyLPIxSELRHtJ5Gec1xkRGccBE6i7SdxTuKPg8disaWlJbvdHo1Gw+Ewy7KBQMDv9/t8Pq+I2dOvvfDCa6dnxdcVmD39Wo05AAAPHz60Nozh4eGJiYn5+XmtMn9484UXDv58SHTlhTf/IHP7D2++QFwZ+vlBUe6hnx8s51ZkYWHh9u3b23g0m83GTTQZhnG5XF6vl5skxWKxRCLBfTipCicVROebxatSJWSPpUmX3cqjc+VviNhWsVU4qfZzdzLpqZaSPAjVSSLP8U4il+/i8bhOp3M6nfF4PBaLRSKRcDgcCoVYlmVZNihisGv//v37uwZF17oGg8HgozOH9u8/dOaRNIcwPQBAmdnZWUfDGBkZmZycXFxcrHCa7Y9v7d//8vs3yUs333+5fO2Pb+0nEvzxrf37ydc3339ZlPvm+y/v3//WH6s4R7e8vHz37t1an4thGIZhnE6ny+XyeDw+ny8QCIRCIXKSlMlkNjY2qvy+O/GAS5uUEKM28XkjMiV5JFr0+STBR3+YnRVblZMKhZo/n0R2AjNC2wwTn/mWNVyhUBDMkwqS5bt0Os2ddDAYDB6Ph/s5Ho+vrq7GYrFoNBqhs/i7118UcvImf/313y2Kkt88WUoAAKiKhYUFT8OYmJiYnp7W6/UVjg18+PaLL77SfUd6kb96p/sVfhB4+0Nhhjvdr4hy3+l+5cUX3/6wigMLRqNxdHR0G4/m9Xp9Pp/f7w8EAizLhsPhSCTC7SRxn5bNZrObm5tN/V7wun4VQqtDOomfKqVSKYvF4vf7eUulUqlkMplMJhP1YfhHL774o+E6FQbALkCn0zVuEvbgwYO5uTmz2WxQK1ar9bPPPqv1udgSoVCIs1EsFuNmSKlUip8kwUnqgqolm83Gsix/MZvNZjKZTCaTrhOWi2/81RsXLfUqDoB2x2QyNW4SNjMzs7CwYLfbm/sZLAUYhpmamtrGo0Wj0Wg0GovFVldX4/F4IpHgZkikkHK5HJykIngnkVpyOBwrKyuff/65KAHPJgDgGWK32+MN49GjR0tLS06n06ZWXC7X9PR09U8kmmUmS3DTo3Q6TQoJTlIdUi05nc5oNPrll19+8cUXX3zxxROCzwEAzxyn05lqGAsLC3q93uPxMGrF5/Nptdpan2uNYH19nbdRNpslhZTP53fR/2e2VRBpye12r66ufvXVV1999dX/CPkSAPDM8Xg8jVsYXFpaMhqN3PdbqpNAIPDo0aNanysjJJvN8jYihQQnqZR8iVwu5/F44vH406dPnz59+hcAQLPx+/0bDUOv13PHmup7nK+OBIPBhYWFHT4mvxBK2iifzxcKBThJpfBvktfrTSQS/yvhKQCgGQQCgcZtVhkMBovFwn2/pTphWXZhYWEbj5ajkSfghj44SdXk83mfz5dKpf4PAKAOWJbNNwyj0Wi1WoPBYJO/rEKeUCi0uLhY36cmBz04Se34/f61tbVm/zMEABQJhUL1HZFJjEajxWJRs5NYlq2Xk6gjHpykdgKBwNraWrP/D8UAgCLhcLhx/95NJlNLzJMa1wNwktphWZZbu2v2v0QAwNZf/vKXUCjUuH/vNpuN20/yer3N3jmiwH050PLycuN6AE5SO6urq5FIpFAoQEsANJenT5+m02mWZRv37z0YDJpMJqfTGQgE6vy1RfXA7/dbLBaDwdC4HnN89YAAAACISURBVICT1E42m41EIizLrgAAmk04HI7H4437957JZFwu19LSEvc/C1cby8vLZrO5oTNFOAkAAFREOp2ORqMhVbKyspJMJnO5XOMeH04CAACgFuAkAAAAagFOAgAAoBbgJAAAAGoBTgIAAKAW4CQAAABqAU4CAACgFuAkAAAAagFOAgAAoBb+H4wkxyppxkZtAAAAAElFTkSuQmCC" /></p></li>
<li>The application will allow you to <b>experiment</b> with different <b>parameters</b> and see results <b>interactively</b>. <br />
<br />
In addition to the "Table View", you will also be able to see the RDF previews for <b>Turtle</b> and <b>N-Triple</b> formats. Both text windows are read-only but allow the user to select text fragments and copy them to the Clipboard by means of the context menu (right-click):<br />
<br />
<img alt="Example of Turtle Output" src="data:image/png;base64,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" title="Example of Turtle Output" width="500" /><br />
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<br />
<img alt="Example of N-Triples RDF Output" 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" width="500" /> <br />
<br />
Bear in mind that the amount of records shown can be modified through the "Preview" menu option.<br />
</li>
<li>Once results look like what is expected, they can be <b>exported</b> in two ways. The first one is through the "Export RDF" menu:<br />
<p align="center"><img alt="Export RDF Menu" title="Export RDF Menu" height="100" src="data:image/png;base64,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" /></p></li>
<li>Alternatively if you click on the "<b>Output folder</b>" field, you will be able to define a directory to export results in a batch mode:<br />
<p align="center"><img alt="Output folder" title="Output folder" height="30" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAh8AAAA2CAIAAADoA2xRAAAKJElEQVR4nO3dTY/beAHH8X0hvfYNmBsHJEBQELuUbdKn4wISSIiC0q5gKQzPSEhs21lVHJkzzLZbmJ0+bB/otJt57Dwlcfwc23H8mDjP5dKq5ZCZxHacTJyxJ27295EPjcf2xNHY33H+zvStNgAAQNjemvQTAACAKYS6AABA+FAXAAAIH+oCADBhzVa71mzZzZbdiN/UbNVbrVbwnUJdAAAmptVulxutot2grfq2XttQq3Gbdo0aV66Xqs1aM1hiUJeYqlQqoiiSJLkLAJNDkqQkSdVqNaIjvdxoZYz6XN748RP55D3hO3djN528J/xoSfoXY/KVeqDAoC5xZJomRVEkSTIMwwLA5FAUtbOzI4piFEd6o9WW7ObftrW37xbOPZI/2ND/vG3GbfrpsvrVReG9x9KdQsWoNUffO9QljliWzeVymqbVarUGAExOtVoVRTGfz0dxpNuN1qZWS9wvJB9K/+TsHaPOlhtcnCbGaqyq9Q93zRN3Cld2dMlujL53qEscZbNZQRCeP3/+PwCYtGazmcvlojjSy43WI9n++iJ/+ZmeMeqiZWtlW6tU4zOpZZs17VWtfuqB9IsVhbbqo+8d6hJHmUymWCy+AIB4IEkyiiPdarTuS5WvLfJ/2jJYq6aV7Wr8SGaFLjfOPZJ/ni6SJuryhstms6VS6RUAxANFUVEc6Y666JxV08sVO35ks8KgLlMjm82qqvoaAOKBpukojvRuXf64pXNWTbMqMSSbZcaqn3skoS7TIJfLoS4A8RF5XTY11qxqVnnSKfEhG2Xaqp97iLpMBdQFIFairssfNjXWsFWzHEOSYVFm7SzqMh1QF4BYibwuzzTGsFXTmnRKfIi6SRnVsw9F1GUadD7sMukDCgD2MAwTxZHuqItK65WSYcWQqJt5o3r2AeoyFUiSRF0A4iPquvx+Q6W1sqKbMVTQDFK3z6AuHgup9LHj6WPH08feEXIHLZy7vtlZ+MLigCVI4URnayk13Ofp/T6oC0CcRF2X322UKLWs6MakU+JDUPWcVjnzoBBmXXqn5iM5pYatMvuO48lPd12WZgi3mSXHV6W58545ABAEy7JRHOnuulhFzRjm1vuew5x4/9bQFcIhqHpOK5+5H1ZduqdR77Q5G/Qjq4vksPP12A7cbPASxKcu+Xxe1/XRfuw7YfHWxNOSpRnkBWB8R1CXvGrJqj7MrUsEkfxow/XYNSMafEnLqWHVxZGW7nm2e+Y9dpxcGHnrB5+vxzLSZhfJzjInrldC22zs6jLyVcnSDEGcn5MOfZABfCFFXpd1hSyZsqoFqYvfnAhwippVrXDq0n1DzHOS7c4P83w9FtSlI8g7XtLceeQFYEwcx0VxpHfr8tt1hVQMqaQN80mnJY45Gx8lPXMiwClqpmSdDqMu6oVB1yjdc2tnDKP/9O2ec+G49421C4vuE/T+8kM2Mupm3VwjLu6teQaTnOv61sWx/Obsol9dHHvhfNEcW9t7SZ27Pzy3FEWNUJcDgyHNne9dsSAvAGOLvC5rSk4xREUd5uYlgkjOrnvmXLrZ+ff6bLL3wGeF9dkkkZxdV29e6g7buJcehC2WMop5+r5w6LoM+/XcHZ5D1qVv2ttOhHVR+9d17mlfXSr92/Gs4r3xwTE05Xgvsfc8ezOHXv2MVJeDr1xcdcHgC8DYoq9LMVvURaUUoC7rs0nn41Hq4kyKZ/UhdZFLu4p5+rPD16X7m/ih69L2vRroH9TpzTnEZvv1rdX/zp7nPUDvZrsvxf79Zr2WdF6c/QW8NUqp7UEjVSFeuwS/Ewx5ARgPz/PDjthxdesys1rMFrWCUhrmxkXXDWPJ2TXnV9dmkwRx8YZ3he5Sa52arA348mCMXNopGsk3qS6O7e+ftTdnyejqsv/knbcmu5+MZ7OuZ+W3vN+FS+9bdLc2+sBPF0VRhmEc8PM+xn3GyAvAWKKuy29W5YysCUVlmBsXCSJxbbX77+4DRVEUZfVagiAu3hi0grJ6LeFewW8NP7SkbMt68jM+vHfGDj3u0v7C1uU4uXC4Oxpomj64LuPcB4bBF4BxRF6XFWlXUgV55LrstcGRiwjrUtyStOS9w9fFMdjgOS16fxkffNIPWJfud/S/JBpps/2Ori7+nwGKvi7IC8AREQQh8GE8AnddSrxcHObjFEEkrq24HhOpj50Pe4/6Vli5lnCt3ZnjWcMPJRa3JC0RRl0O+rxL9+zsuZRxZGlIGBwb3z8pe4Y3xttsv8HjLt2dGj7u0jcC7xjkd4+7OIuVu056RvVddRlt3IVhGNM0D/6R9wzbjwJ5AQjuKOoilnhJHmY+RRCJq8ueOURqfsCX51OEY87y1YRn7eWrid7ag+ULcnh1aXvusvW+57Nv4C1V/W9q9c6zB/8VgLE2O3gXHAUKes/YgOV9e9O3I751GfGesVHr8tr3o/qvl2Y6M/zjg8EXgKCirsuvV6SdQokTA9alE4j9ee6azKcIIqy6bIpqeHVpt/tPnX4XCo7zr+PzK84lnTfmej7v4vooCXm4zfYb8GnKgJ93cb4I5ILfoJH3zuOBrWq322Ffu+yR5s77/pmxAZc2yAtAQIVCYdgRO65eXZbF7YLCitIw8ymCSFxJ98/szk1fSXRPAql59wrpKwnP2ukrCYJIzQ/9npIkkYL0rFBK3OVi/zeSj+rj7m80lmWD1CUo5AUgmKjrcnlZ3BaKbEGMoRwvoi7TI+K6YPAFIJjI65IWt/giI0y6JH5yvLghKKdQl+kQdV0AIBBRFKM40rt1+VW6sMnLtFCIoSxXWOeLp+6yqMs0YFnWsqxJH1AAsCfyunwuPOMkmhdiKMMKa5x86k786wIj4DgOdQGIjyOpi0hxky6Jn05d3kVdpgPqAhArUdflg8+FDVakOD6GMgy/xkqoy5RAXQBiRZKkKI70Xl2e8htMIc9OuiR+dhlulRXfvc2gLtOA5/lyuTzpAwoA9kRdl18+5dcZIc9yMbRLcysM6jItUBeAWJFlOYoj3VMXkmFjaIdmUZfpIQgC6gIQH5HX5Qm/SgtZetIl8bNFsct04Xuoy3QQRdGyrFevXk36mAKA1y9evIjonbFyo/VEsb95m//Zf9lHpLCeZ3coZjdO01aeWSbZhYzwjf/Ql5YV2kJd3nCapimK0m63ERiAyXr58qVt2xHdM2Y3Wzmz/oPH0ncX6b+v8w9JIU3xy5QQnymd529n+ctP2C/dpP6yqUp2Y/S9Q13iqFarKYoiiuJB//MCAEROlmVd16M40putlmw35kjjW4vclz+h3v6UPrkYu+nbC9RX/k1//7H4qVA26s3R9w51AQCYmEqjlTFq/yCNi8vFHy5J7z2O3fSTp/Jft9Q7YkWoNOqtALuGugAATEyr3a40Wkq1QZr1Da22plbjNm3rNaHS0OvNQGlpoy4AABAF1AUAAMKHugAAQPhQFwAACB/qAgAA4UNdAAAgfKgLAACE7/+wxG/kTilkywAAAABJRU5ErkJggg==" /></p>Once an output directory has been chosen, you may click on the <img alt="Run" height="22" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAD8AAAAtCAIAAACWIyQYAAAEyElEQVRogdWZy2/cVBSH+zeZHQskQLxERWjsttkWkEBCCDRJBaW8XxISpJkuuqRrmCYttHk1TUjTzmQeyUxmMjN+Xvva1+/H2B7PH8AidF61b1qp0bifzsLnXP/kzwt7c071nmdODTfdqOd3I68beWH6qhsFURTF2ke9nhNGihcydlAz/IrWSVvVTZ93ArXT9bvRuL0TRg0zuN42P9qRp9bB22upq6l18OF9+BdrCW7Qf4FTvV4vjHrQ6/5W00+viTNb8qWK8XPNSlt9UtBeXgYXtuGq6Jp+d2DvhdG+7pMbIrUJ/+S9AzPgnJBPU7F2WNSC3+vWG6vi/IEBvXBg74TRluy9uixc3jMaZiDZnu54uttJT2mOx1leUQ+m78HPdxFjBwN7O4w2oPvKsvBT1eRsX3e8TvqAlss44cyW/FleaVnx9gZv+4bjeulDtlwWb/9j1eBtX7fdFCJbDmsHM1sw2X5f56yObjuTVo1BNh3GDmY2k+1/2Nc509MsJ4VA06Yt/zzOfk9nTU+z7EmrxiAZFm12zm9KGHuNMVzVtFOIZFhts3P+XrL99xWN0R1kWClE1M2W4Z3D2H9XUWnNQYY5adUYgGY0dffcPRFvbyu6iePWRWKMi7ewgWcD0Iym7pzbwNq3NVvWDBy35giCuloZ6UcGJ4Og6k0Nb19GLdWSNf1p7OMmJwCPtEPNxtl/W0YtZEJVx3HzyHVoUrlKjU1OAB5pDdU+i7MvoSYyJaThWJojCCpbHpvMLR1dl7PUoIkJlLMUQWXL2tJc/7MZvTsJTlEbyDq7ATD2yqFiSEh9Cvtylhrun8R+WHksjrGX1Tqyzt5Ntv+mqBwquohUHIuzIz8cKlsaPi1lKYKYXRwP9O8qHdmWEo6TYWX1QDEpjP3XRbkh60BBOBZnCYJcKPav+w1CCKHiAkkQs4tJAVRcIEcDcYk4GIhqskHdFZLtd2EdakB+Yvv/nz2kc4L2ShXq1Pox9qogKzhuZAiCXNgd6YnMjeF20D0W2F0gR9JHk7FEHLSkVKFOHmMvqQKUceQyBEFeKYxNiEwu4TiXIYYmhSvkWLpwhRykk2mL8jH2X+3CA1Hlpae0PxJ4NBu1zWUI4lnZ70sa1r4g1UTESRBHLkMQ5Hz+8WF/mp8n+7+kTG40kJ8nx9L5eZIgMjnsMyGELQD3RJVc4xPtLxekGlA4UUohTUE6zj4vVQWFBZM2jaMpSBWApjH2X+bFfUFmgJhCDnmxLCjTa1yy/UOwx0NGACmkwYESL0+vHmMv0fykTeM4sj+Dsb/0EFQ4ieaFFNJghRIHsfYPhAortrlJm8ZRZ/kiJ51ZYRPtv3gglFnQ5vgUUmf4XfYJ7Fssl0IOGO44+x2hyIBDZtKmcVRprsCI7z5u74TRDvJeXxE+/ZfbaoFymzug2XqaqtpmCy3udgO89g8zVxjdPnjdqGkF72/Dd5aZa2VhswXytFCgQXoq3xZWDoXLO9wLS/Qv+9rI5qcbRbIXXm+Zby7zL96kT99hppZTV2/dpl/6m3lvW7oDHDMY2rr1ej03jBqm/0fLnC0oH9yHF7ZTVx8/kH+taquSC9wweLSxHWyb3TBCnbBlBRXdL2mdtFXN8IEbGkE3GNqXj2z6nzv+A3ey5bnUIpZdAAAAAElFTkSuQmCC" /> button. If you do so, the application will process all the files previously chosen with the <img alt="File(s)..." height="22" src="data:image/png;base64,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" title="File(s)..." /> or <img alt="Folder..." height="22" src="data:image/png;base64,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" title="Folder..." /> buttons and will export them in Turtle and N-Triple formats in the output folder.</li>
</ul><br />
<h3>Fields</h3><br />
As it was mentioned before, the "<b>Fields</b>" tab lets you define a set of headers in case the file does not have a header line or if you would like to customise the list:<br />
<br />
<p align="center"><img alt="Fields" src="data:image/png;base64,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" title="Fields" width="300" /></p><ul><li>To <b>add</b> a <b>row</b>, enter its "Name" and "Type". Optionally, you can also specify a prefix for the predicate and/or the objects associated. Once that is done, click on <img alt="Add Row" title="Add Row" height="22" src="data:image/png;base64,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" /> button.</li>
<li>To <b>update</b> an existing <b>row</b>, first select it from the list with a mouse click. You will see that "Prefix predicate", "Prefix object", "Name" and "Type" fields are populated with the relevant data. Make the changes that you would like to perform and finally click on <img alt="Update Row" title="Update Row" height="22" src="data:image/png;base64,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" /> button.</li>
<li>To <b>delete</b> a <b>row</b>, first select it from the list and then click on <img alt="Delete" title="Delete" height="22" src="data:image/png;base64,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" /> button.</li>
<li>To <b>change</b> the <b>order</b> of a field, first select it and then click on <img alt="Up" title="Up" height="22" src="data:image/png;base64,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" /> or <img alt="Down" title="Down" height="22" src="data:image/png;base64,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" /> to move it up or down respectively.</li>
<li>If you have a file with a myriad of fields and there is no header line, you can <b>automatically generate</b> the <b>list of columns</b>. In order to do so, just click on <img alt="Auto generate" title="Auto generate" height="22" src="data:image/png;base64,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" /> button. The application will count the number of fields (according to the delimiter specified in the "General Settings" tab) and will assign names automatically to each of them, using one or more letters of the alphabet, exactly in the same vein as columns in spreadsheet applications.</li>
<li>You do not know which <b>field types</b> you should use? No worries! Just click on the <img alt="Detect types" title="Detect types" height="22" src="data:image/png;base64,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" /> button and the application will suggest the <b>most appropriate choice for each field</b> within the list of 15 supported types.</li>
</ul><br />
<h3>Prefixes</h3><br />
Would you like to improve the readability of your output files? <b>Prefixes</b> are the answer: <br />
<p align="center"><img alt="Prefixes" src="data:image/png;base64,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" title="Prefixes" width="300" /></p><ul><li>To <b>add</b> a <b>prefix</b>, just enter the "Name" and "Reference" of the prefix and click on <img alt="Add prefix" title="Add prefix" height="22" src="data:image/png;base64,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" /> button.</li>
<li>To <b>delete</b> a <b>prefix</b>, select it on the list and click on the <img alt="Delete" title="Delete" height="22" src="data:image/png;base64,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" /> button.</li>
<li>If you need to <b>update</b> a <b>prefix</b>, as setting are quite simple, you can delete it and add it again.</li>
</ul><br />
<h3>RDF Usine in Perspective</h3><p>RDF Usine is not meant to compete with other open source or commercial tools. It is just the result of a small-sized project I had to undertake during my studies at <i>École Centrale Paris</i> and it could be considered as a proof of concept. That being said, it still depicts some interesting features that might hopefully represent a source of inspiration for enhancing other open source projects.</p><p>In the following paragraphs, I will outline a brief comparison with regards to two major open source projects: <i>Any23</i> and <i>Datalift</i>.</p><b><u>Any23</b></u><br />
<ul><li>It is an Apache Foundation project.</li>
<li>It enjoys the benefits of community contributions (bug fixes, enhacements, etc).</li>
<li>It is library, a RESTful web service and a command line tool that extracts structured data in RDF format from a variety of Web documents.</li>
<li>It can be embedded in another application but it does not provide a graphic user interface. Therefore, it does not offer interactivity or previews.</li>
<li>It supports a number of input formats including HTML, Turtle, N-Triples, N-Quads, RDF/XML, CSV (provided a header is available).</li>
<li>The following output formats are supported: Turtle, N-Triples, N-Quads, RDF/XML, JSON</li>
</ul><b><u>Datalift</b></u> <br />
<ul><li>It is an open platform and is the result of an R&D project leaded by INRIA and sponsored by other academic, industry, institutional and innovation partners such as <i>Montpellier Laboratory of Informatics</i>, <i>Eurecom</i>, <i>Atos</i>, <i>Mondeca</i>, <i>Institut National de l'Information Géographique et forestiére</i> and <i>L'Institut National de la Statistique et des Études Économiques</i>.</li>
<li>It enjoys the benefits of community contributions (bug fixes, enhacements, etc).</li>
<li>It is available in French.</li>
<li>It runs as a web application and may be used by means of a browser either locally or remotely.</li>
<li>It has an embedded RDF triplestore.</li>
<li>SPARQL queries may be issued through its web interface.</li>
<li>Imported and processed data is organised in Projects.</li>
<li>Once a file has been imported, there is not much flexibility when it comes to performing changes. For example, to change a column name or type, the file needs to be removed and imported again.</li>
<li>As input, it allows CSV, RDF, relational databases (through JDBC connectors), other SPARQL endpoints, XML, Shapefile and GML.</li>
<li>Among other features, it allows exports to RDF and CSV formats, publishing data towards other triple stores, etc.</li>
<li>It allows a broad range of chart visualisations on the basis of SPARQL queries.</li>
<li>A base URI and a Graph URI may be defined. This feature is not available in RDF Usine.</li>
</ul><b><u>RDF Usine</b></u><br />
<ul><li>It is conceived as a stand alone application with which the user can interact directly by means of its Graphic User Interface.</li>
<li>It may be used in three languages: English, French and Spanish.</li>
<li>It only supports delimited field files as input but it does not require headers to be present (as it happens with Apache Any23, for instance).</li>
<li>It focuses on the CSV to RDF translation but it does not offer an embedded triple store or querying facilities. However, once the conversion has taken place, it is possible to export data as triples or SPARQL 1.1 Insert statements and upload them to a triple store such as Apache Fuseki.</li>
<li>Different settings may be tested interactively, as the application allows previews both in RDF and tabular formats. For the time being, this is not available in Any23 or Datalift.</li>
<li>Configurations can be Saved and Loaded, thus facilitating reutilisation. It is also possible to process multiple files and/or entire folders (and their respective subfolders) with the same set of settings. This is not the case in DataLift: if you have to process many files, you have to tackle them individually even if they have identical formats.</li>
<li>Entity types (i.e., the object associated to "a" predicates) may be freely defined. This is not allowed in Datalift.</li>
<li>Objects identifiers (i.e., subjects) may be autonumeric or read from a particular column. An optional prefix may also be specified. Datalift only allows autonumeric values.</li>
<li>It is possible to define Prefix names and URIs manually.</li>
<li>Automatic field type detection is a very nice feature that can save up a lot of time while defining conversion parameters. It is also meant to aid users that do not know the differences between the different types. This is not available in Datalift either.</li>
</ul><b><u>Other RDF Conversion Tools</b></u><br />
<ul><li>You may find information about other RDF Conversion Tools in <a href="http://www.w3.org/wiki/ConverterToRdf">W3 website</a>.</li>
</ul><br />
<h3>Summing-up</h3><p><b><i>RDF Usine</i></b> is a tool that allows <b>converting</b> text files into <i>Turtle</i> or <i>N-Triple</i> <b>RDF</b> files in an <b>easy</b> and <b>interactive</b> way. Both <a href="https://rdf-usine.googlecode.com/svn/trunk/executable/rdf-usine.jar">executables</a> and the <a href="https://code.google.com/p/rdf-usine">source code</a> are published and available for the general public. </p><p>If you would like to <b>contribute</b> to the project by fixing bugs or developing enhancements, do not hesitate to let me know.</p><p>Please notice that this software is provided <b>"as is"</b> and any expressed or implied warranties are disclaimed. In no event shall the contributors be liable for any direct, indirect, incidental, special, exemplary or consequential damages arising in any way out of the use of this software.</p><p>In my next post, I will present an interesting <b>real-world use case</b> of heterogeneous <b>data integration</b> where <i>RDF</i> shows its full potential.</p><p>Please <b>share</b> this post with friends and colleagues and let me know <b>what you think</b> by using the <b>Comments</b> section below!</p><br />
<h3>Bonus Track: Behind the Scenes</h3><p>Some details for the technically curious readers:<p><ul><li>The application is made up by about <b>3,500 lines</b> of Java code.</li>
<li>The class for popups (windows to display errors, warnings or additional information) was a slight adaptation of code publicly shared by <b>Mark Heckler</b>. This accounts about <b>300 lines</b> of Java code.</li>
<li>The interface was defined as an FXML file of about <b>350 lines</b> of code.</li>
<li><b>Internationalisation</b> settings are stored as <b>key-value</b> files. There is one file per language and each of them contains almost <b>100 labels</b>. Most of the labels represent single words or short sentences.</li>
<li>For String tokenisation purposes, <b>Apache Commons Lang</b> library is used. It is governed by the terms of the Apache License version 2.0.</li>
<li>The initial version of the application used a <b>third-party library</b> for <b>outputting RDF</b> text but its usage was <b>discontinued</b> when performance problems were detected with big files.</li>
<li>Binaries are as slim as <b>88 KB</b>. Considering the size of the aforementioned library (373 KB), the application total size is <b>461 KB</b>.</li>
<li>Converting a file of almost <b>691,000 lines</b> and <b>8 columns</b> (33.6 MB) to <b>Turtle</b> takes about <b>30 seconds</b> and results in an output RDF file of 216 MB. The same conversion to <b>N-Triples</b> takes about <b>36 seconds</b> and outputs 256 MB.</li>
</ul><br />
<h3>About the Author</h3><p><i><b>Maximiliano Ariel López</b></i> is a Bachelor of <b>Business Information Systems</b> with <i>9 years</i> of <b>work experience</b> in Information Technology and a background of almost <i>7 years</i> of <b>university teaching</b>. This gave him exposure to different organisational environments and to a wide range of IT tools, thus testing his abilities to adapt and quickly learn.</p><p>At present, he is attending the third semester of the <i>Erasmus Mundus</i> Master's Programme in <i><b>Information Technologies for Business Intelligence (IT4BI)</b></i> at <i><b>École Centrale Paris</b></i>.</p><p>You may <b>contact</b> him through <a href="http://www.linkedin.com/in/maximilianoariellopez"><b>LinkedIn</b></a> and <a href="https://plus.google.com/+MaximilianoArielLópez/about"><b>Google+</b></a>.</p><p/>IT Sharedhttp://www.blogger.com/profile/11763288513127086508noreply@blogger.com1Châtenay-Malabry, France48.771896 2.27074800000002648.730034499999995 2.1900670000000257 48.8137575 2.3514290000000262