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Book Information


Title: Data-Intensive Text Processing with MapReduce
Written By: Jimmy Lin‌
Chris Dyer‌
Edited By: Graeme Hirst
Series Title: Synthesis Lectures on Human Language Technologies

Our world is being revolutionized by data-driven methods: access to large
amounts of data has generated new insights and opened exciting new
opportunities in commerce, science, and computing applications. Processing
the enormous quantities of data necessary for these advances requires large
clusters, making distributed computing paradigms more crucial than ever.
MapReduce is a programming model for expressing distributed computations
on massive datasets and an execution framework for large-scale data
processing on clusters of commodity servers. The programming model
provides an easy-to-understand abstraction for designing scalable algorithms,
while the execution framework transparently handles many system-level
details, ranging from scheduling to synchronization to fault tolerance. This
book focuses on MapReduce algorithm design, with an emphasis on text
processing algorithms common in natural language processing, information
retrieval, and machine learning. We introduce the notion of MapReduce
design patterns, which represent general reusable solutions to commonly
occurring problems across a variety of problem domains. This book not only
intends to help the reader "think in MapReduce", but also discusses
limitations of the programming model as well.

Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm
Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM
Algorithms for Text Processing / Closing Remarks

Publication Year: 2010
Publisher: Morgan & Claypool Publishers
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BibTex: View BibTex record
Linguistic Field(s): Computational Linguistics
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Format: Electronic
ISBN-13: 9781608453436
Pages: 177
Prices: U.S. $ 30.00
Format: Paperback
ISBN-13: 9781608453429
Pages: 177
Prices: U.S. $ 40.00