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Data-Intensive Text Processing with MapReduce

2022-05-31 Computers
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Release : 2022-05-31
Genre : Computers
Kind : eBook
Book Rating : 363/5 ( reviews)

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Book Synopsis Data-Intensive Text Processing with MapReduce by : Jimmy Lin

Download or read book Data-Intensive Text Processing with MapReduce written by Jimmy Lin. This book was released on 2022-05-31. Available in PDF, EPUB and Kindle. Book excerpt: 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

Data-intensive Text Processing with MapReduce

2010 Computers
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Release : 2010
Genre : Computers
Kind : eBook
Book Rating : 421/5 ( reviews)

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Book Synopsis Data-intensive Text Processing with MapReduce by : Jimmy Lin

Download or read book Data-intensive Text Processing with MapReduce written by Jimmy Lin. This book was released on 2010. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering and Computer Science. Synthesis Lectures provide concise, original presentations of important research and development topics, published quickly, in digital and print formats. For more information visit www.morganclaypool.com --Book Jacket.

Data-Intensive Text Processing with MapReduce

2010-10-10 Computers
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Release : 2010-10-10
Genre : Computers
Kind : eBook
Book Rating : 43X/5 ( reviews)

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Book Synopsis Data-Intensive Text Processing with MapReduce by : Jimmy Lin

Download or read book Data-Intensive Text Processing with MapReduce written by Jimmy Lin. This book was released on 2010-10-10. Available in PDF, EPUB and Kindle. Book excerpt: 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

Data-intensive Systems

2019-01-01 Computers
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Release : 2019-01-01
Genre : Computers
Kind : eBook
Book Rating : 036/5 ( reviews)

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Book Synopsis Data-intensive Systems by : Tomasz Wiktorski

Download or read book Data-intensive Systems written by Tomasz Wiktorski. This book was released on 2019-01-01. Available in PDF, EPUB and Kindle. Book excerpt: Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before continuing with independent work and the more advanced technical reference literature that dominates the current landscape. The material in the book is structured following a problem-based approach. This means that the content in the chapters is focused on developing solutions to simplified, but still realistic problems using data-intensive technologies and approaches. The reader follows one reference scenario through the whole book, that uses an open Apache dataset. The origins of this volume are in lectures from a master’s course in Data-intensive Systems, given at the University of Stavanger. Some chapters were also a base for guest lectures at Purdue University and Lodz University of Technology.

Data Intensive Computing Applications for Big Data

2018-01-31 Computers
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Release : 2018-01-31
Genre : Computers
Kind : eBook
Book Rating : 140/5 ( reviews)

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Book Synopsis Data Intensive Computing Applications for Big Data by : M. Mittal

Download or read book Data Intensive Computing Applications for Big Data written by M. Mittal. This book was released on 2018-01-31. Available in PDF, EPUB and Kindle. Book excerpt: The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

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