Data-Intensive Text Processing with MapReduce

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...

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Detalles Bibliográficos
Autores principales: Lin, Jimmy, Dyer, Chris
Lenguaje:eng
Publicado: Morgan & Claypool Publishers 2010
Materias:
Computing and Computers
Acceso en línea:http://cds.cern.ch/record/1486557
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author Lin, Jimmy
Dyer, Chris
author_facet Lin, Jimmy
Dyer, Chris
author_sort Lin, Jimmy
collection CERN
description 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-underst
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2010
publisher Morgan & Claypool Publishers
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spelling cern-14865572021-04-22T00:17:02Zhttp://cds.cern.ch/record/1486557engLin, JimmyDyer, ChrisData-Intensive Text Processing with MapReduceComputing and ComputersOur 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-understMorgan & Claypool Publishersoai:cds.cern.ch:14865572010
spellingShingle Computing and Computers
Lin, Jimmy
Dyer, Chris
Data-Intensive Text Processing with MapReduce
title Data-Intensive Text Processing with MapReduce
title_full Data-Intensive Text Processing with MapReduce
title_fullStr Data-Intensive Text Processing with MapReduce
title_full_unstemmed Data-Intensive Text Processing with MapReduce
title_short Data-Intensive Text Processing with MapReduce
title_sort data-intensive text processing with mapreduce
topic Computing and Computers
url http://cds.cern.ch/record/1486557
work_keys_str_mv AT linjimmy dataintensivetextprocessingwithmapreduce
AT dyerchris dataintensivetextprocessingwithmapreduce

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