Date of Original Version

December 2012

Type

Article

Abstract or Description

This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete data. We formulate the classical Expectation Maximization (EM) algorithm within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present details of the MapReduce formulation of EM, report speed-ups versus the sequential case, and carefully compare various Hadoop cluster configurations in experiments with Bayesian networks of different sizes and structures.

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Published In

Proc. of Big Learning: Algorithms, Systems and Tools.

 

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