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2017 | OriginalPaper | Chapter

A Functional Approach to Parallelizing Data Mining Algorithms in Java

Authors : Ivan Kholod, Andrey Shorov, Sergei Gorlatch

Published in: Parallel Computing Technologies

Publisher: Springer International Publishing

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Abstract

We describe a new approach to parallelizing data mining algorithms. We use the representation of an algorithm as a sequence of functions and we use higher-order functions to express parallel execution. Our approach generalizes the popular MapReduce programming model by enabling not only data-parallel, but also task-parallel implementation and a combination of both. We implement our approach as an extension of the industrial-strength library Xelopes, and we illustrate it by developing a multi-threaded Java program for the 1R classification algorithm, with experiments on a multi-core processor.

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previous chapter Parallel Computing for Time-Consuming Multicriterial Optimization Problems
next chapter Parallel Calculation of Diameter Constrained Network Reliability
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Metadata
Title
A Functional Approach to Parallelizing Data Mining Algorithms in Java
Authors
Ivan Kholod
Andrey Shorov
Sergei Gorlatch
Copyright Year
2017
Book
Parallel Computing Technologies

Print ISBN: 978-3-319-62931-5

Electronic ISBN: 978-3-319-62932-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-62932-2_44

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