Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies) - Tapa blanda

Lin, Jimmy; Dyer, Chris

 
9781608453429: Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies)
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ISBN 10: 1608453421 ISBN 13: 9781608453429
Editorial: Morgan and Claypool Publishers, 2010

Sinopsis

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

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

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

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Morgan and Claypool Publishers
Año de publicación
2010
Idioma
Inglés
ISBN 10 
1608453421
ISBN 13 
9781608453429
Encuadernación
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Número de páginas
178
Contacto del fabricante
no disponible
Persona responsable
no disponible

Otras ediciones populares con el mismo título

9783031010088: Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies)

Edición Destacada

ISBN 10:  3031010086 ISBN 13:  9783031010088
Editorial: Springer, 2010
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