Overview

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 ofMapReduce 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

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9783031010088

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Jimmy Lin is an Associate Professor in the iSchool (College of Information Studies) at the University of Maryland, College Park. He directs the recently-formed Cloud Computing Center, an interdisciplinary group that explores the many aspects of cloud computing as it impacts technology, people, and society. Lin's research lies at the intersection of natural language processing and information retrieval, with a recent emphasis on scalable algorithms and large-data processing. He received his Ph.D. from MIT in Electrical Engineering and Computer Science in 2004.

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