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Data-Intensive Text Processing with MapReduce [electronic resource] / by Jimmy Lin, Chris Dyer.

By: Lin, Jimmy [author.].
Contributor(s): Dyer, Chris [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Human Language Technologies: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2010Edition: 1st ed. 2010.Description: IX, 171 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031021367.Subject(s): Artificial intelligence | Natural language processing (Computer science) | Computational linguistics | Artificial Intelligence | Natural Language Processing (NLP) | Computational LinguisticsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- MapReduce Basics -- MapReduce Algorithm Design -- Inverted Indexing for Text Retrieval -- Graph Algorithms -- EM Algorithms for Text Processing -- Closing Remarks.
In: Springer Nature eBookSummary: 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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Introduction -- MapReduce Basics -- MapReduce Algorithm Design -- Inverted Indexing for Text Retrieval -- Graph Algorithms -- EM Algorithms for Text Processing -- Closing Remarks.

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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