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<!DOCTYPE html>
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<meta name="description" content="Course homepage for CS 489 Big Data Infrastructure (Winter 2017) at the University of Waterloo">
<meta name="author" content="Jimmy Lin">
<title>Big Data Infrastructure</title>
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<h1>Assignments <small>CS 489/698 Big Data Infrastructure (Winter 2017)</small></h1>
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<li><a href="project.html">Final Project</a></li>
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<h3>Final Project</h3>
<p>The final project is a requirement only for graduate students taking CS 698.</p>
<p>The topic of the final project can be on anything you wish in the
space of big data. Anything reasonably related to topics that we
covered in the course is within scope. For reference, there are three
types of projects you might consider:</p>
<ul>
<li>Implement a big data algorithm in MapReduce or Spark: choose a
particular big data algorithm (for processing text, graphs,
relational data, etc.) and implement it. Ideally, the implementation
does not already exist in a library or open-source package. Since we
want you to implement the algorithm from scratch, it might perhaps
be too tempting to simply copy existing
code—see <a href="organization.html">notes on academic
integrity</a>.</li>
<li>Learn and explore a (new) big data processing framework:
although we discussed a variety of processing frameworks in class,
the assignments focused on MapReduce and Spark. Here's your chance
to learn a new processing framework, e.g., Spark Streaming, GraphX,
Giraph, Flink, etc. The project would involve learning to use the
processing framework and doing something interesting with it. The
"something interesting" might be a data mining algorithm, although
note that the expectations would be lower than building something in
MapReduce or Spark, since learning the new framework would form an
essential component of the project.</li>
<li>Perform some interesting data science. Is there a particular
dataset you'd like to explore or analyze? Your project could involve
performing interesting analytics on a dataset—here, the focus
would be the analytical product and the insights gleaned, as opposed
to the raw algorithms themselves. However, a superficial analysis
with existing machine-learning libraries is not enough.</li>
</ul>
<p>You may work in groups of up to three, or you can also work by
yourself if you wish. The amount of effort devoted to the project
should be proportional to the number of people in the team. We would
expect a level of effort comparable to two assignments per person.</p>
<p>When you are ready, send the
instructors <code>uwaterloo-bigdata-2017w-staff@googlegroups.com</code>
an email describing what you'd like to work on. We will provide you
feedback on appropriateness, scope, etc. The "soft" deadline for this
proposal is March 16, 2017—as in, there are no explicit
penalties if you miss this deadline, but it is of course in your best
interest to communicate with us in a timely fashion.</p>
<p>In terms of resources, you are welcome to use the Altiscale
cluster. Note that we expect your project to be more than a "toy". To
calibrate what we mean by "toy", consider the assignments throughout
the course: they have a "run on local" part and "run on Altiscale"
part. The first part is "toy"; the Altiscale part would not be. If
you're planning to work with a framework that doesn't run on
Altiscale, you're responsible for finding your own hardware
resources.</p>
<p>The deliverable for the final project is a report. Use
the <a href="http://www.acm.org/publications/proceedings-template">ACM
Templates</a>. The contents of the report will of course vary by the
topic, but we would expect the following sections:</p>
<ul>
<li>describe the problem you're tackling and what you're trying to
accomplish (introduction, problem statement)</li>
<li>present existing solutions (background, related work)</li>
<li>detail how you went about solving the problem (methods,
algorithms, implementation details, etc.)</li>
<li>discuss how well things work (some sort of evaluation and results).</li>
</ul>
<p>Once again, length would vary, but 6 pages (in the ACM Template)
seems about right.</p>
<p>The project deadline is 1pm Thursday, April 13, 2017. Send your
final project report
to <code>uwaterloo-bigdata-2017w-staff@googlegroups.com</code>.</p>
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