| 000 | 03134nam a22003735i 4500 | ||
|---|---|---|---|
| 001 | 387362 | ||
| 003 | ES-MaUEC | ||
| 005 | 20230102122329.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2010 sz | s |||| 0|eng d | ||
| 020 | _a9783031021367 | ||
| 024 | 7 |
_a10.1007/978-3-031-02136-7 _2doi |
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| 040 |
_aES-MaUEC _bspa _cES-MaUEC |
||
| 050 | 4 |
_aQA76.9.D3 _b2010 |
|
| 100 | 1 |
_aLin, Jimmy _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aData-Intensive Text Processing with MapReduce _cby Jimmy Lin, Chris Dyer. |
| 250 | _a1st edition 2010 | ||
| 264 | 1 |
_aCham _bSpringer International Publishing _c2010 |
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| 300 |
_a1 recurso en línea (IX, 171 páginas) _b |
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| 336 |
_atexto _btxt _2rdacontent |
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| 337 |
_aelectrónico _bc _2rdamedia |
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| 338 |
_arecurso electrónico _bcr _2rdacarrier |
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| 347 |
_aarchivo de texto _bPDF |
||
| 490 | 0 |
_aSynthesis Lectures on Human Language Technologies _x1947-4059 |
|
| 505 | 0 | _aIntroduction -- MapReduce Basics -- MapReduce Algorithm Design -- Inverted Indexing for Text Retrieval -- Graph Algorithms -- EM Algorithms for Text Processing -- Closing Remarks. | |
| 520 | _aOur 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. | ||
| 700 | 1 |
_aDyer, Chris _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 776 | 0 | 8 |
_iPrinted edition: _z9783031010088 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031032646 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02136-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 988 | _aSynthesis Collection of Technology_2010 | ||
| 999 |
_c387362 _d387362 |
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