Livre: Lin & Dyer, Data-Intensive Text Processing with MapReduce

[Thierry Hamon]

Date: Mon, 21 Jun 2010 17:22:32 -0400
From: Graeme Hirst <gh at cs.toronto.edu>
Message-Id: <46047507-7DD8-4279-8115-DB7404DC9759 at cs.toronto.edu>
X-url: http://dx.doi.org/10.2200/S00274ED1V01Y201006HLT007


BOOK ANNOUNCEMENT


Data-Intensive Text Processing with MapReduce

Jimmy Lin and Chris Dyer
(University of Maryland)

Synthesis Lectures on Human Language Technologies #7 (Morgan &
Claypool Publishers), 2010, 177 pages

Abstract

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

http://dx.doi.org/10.2200/S00274ED1V01Y201006HLT007


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