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[eu_members at aclweb dot org] Data-Intensive Text Processing with MapReduce -- Book Announcement



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


This title is available online without charge to members of institutions that have licensed the Synthesis Digital Library of Engineering and Computer Science.  Members of licensing institutions have unlimited access to download, save, and print the PDF without restriction; use of the book as a course text is encouraged.  To find out whether your institution is a subscriber, visit <http://www.morganclaypool.com/page/licensed>, or just click on the book's URL above from an institutional IP address and attempt to download the PDF.  Others may purchase the book from this URL as a PDF download for US$30 or in print for US$40.  Printed copies are also available from Amazon and from booksellers worldwide at approximately US$40 or local currency equivalent.