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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.
Drug Design Exemplar
An important problem in the biological sciences is that of drug design: finding small molecules, called ligands, that are good candidates for use as drugs. We introduce the problem and provide several different parallel solutions, in the context of parallel program design patterns.
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.