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The Module Collection
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Concurrency and Map-Reduce Strategies in Various Programming Languages
Professor Richard Brown, St. Olaf College
This concept module explores how concurrency and parallelism have been established in programming languages and how one can implement map-reduce in several high-level programming languages taught in a CS curriculum, including Scheme, C++, Java, and Python.
Map-reduce Computing for Introductory Students using WebMapReduce
Professor Richard Brown, St. Olaf College Professor Libby Shoop, Macalester College
This module emphasizes data-parallel problems and solutions, the so-called 'embarrassingly parallel' problems where processing of input data can easily be split among several parallel processes. Students use a web application called WebMapReduce (WMR) to write map and reduce functions that operate on portions of a massive dataset in parallel.
WMR Exemplar: UK Traffic Incidents
Using data published by the United Kingdom department of Transportation about traffic incidents, students can explore and perform analyses using map-reduce techniques.
WMR Exemplar: Flickster network data
The exercises in this module use a network of friendships on the social movie recommendation site Flixster. Students will use it to learn how to analyze networks and chain jobs, using the WebMapReduce interface.
WMR Exemplar: LastFM million-song dataset
This module demonstrates how hadoop and WMR can be used to analyze the lastFM million song dataset. It incorporates several advanced hadoop techniques such as job chaining and multiple input.