Data-intensive text processing with MapReduce / Book / Jimmy Lin and Chris Dyer.

By:

Lin, Jimmy, 1979-

Contributor(s):

Dyer, Chris

Material type: Text

TextSeries: Synthesis lectures on human language technologies ; #7Publication details: San Rafael : Morgan & Claypool, ♭2010.Description: ix, 165 pages : 23 cm. 24 cmISBN:

9781608453429 (paperback)
9781608453429 (pbk.)

Subject(s):

DDC classification:

Other classification:

Summary: 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

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	Junaid Zaidi Library, COMSATS University Islamabad Ground Floor	004 LIN-D 62556 (Browse shelf(Opens below))	Available		10001000062556

Total holds: 0

Includes bibliography and author's biography

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

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