Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies, 7)

Lin, Jimmy; Dyer, Chris

ISBN 10: 1608453421 / ISBN 13: 9781608453429

Published by Morgan and Claypool Publishers, 2010

About this Item

The item is very worn but is perfectly usable. Signs of wear can include aesthetic issues such as scratches, dents, worn and creased covers, folded page corners and minor liquid stains. All pages and the cover are intact, but the dust cover may be missing. Pages may include moderate to heavy amount of notes and highlighting, but the text is not obscured or unreadable. Page edges may have foxing age related spots and browning . May NOT include discs, access code or other supplemental materials.

Seller Inventory # BVV.1608453421.A

Bibliographic Details

• Title: Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies, 7)
• Publisher: Morgan and Claypool Publishers
• Publication Date: 2010
• Language: English
• ISBN 10: 1608453421
• ISBN 13: 9781608453429
• Binding: Soft cover
• Condition: acceptable

About this title

Synopsis

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