| 000 | 03628nam a22005295i 4500 | ||
|---|---|---|---|
| 001 | 978-3-031-02136-7 | ||
| 003 | DE-He213 | ||
| 005 | 20240730163820.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220601s2010 sz | s |||| 0|eng d | ||
| 020 |
_a9783031021367 _9978-3-031-02136-7 |
||
| 024 | 7 |
_a10.1007/978-3-031-02136-7 _2doi |
|
| 050 | 4 | _aQ334-342 | |
| 050 | 4 | _aTA347.A78 | |
| 072 | 7 |
_aUYQ _2bicssc |
|
| 072 | 7 |
_aCOM004000 _2bisacsh |
|
| 072 | 7 |
_aUYQ _2thema |
|
| 082 | 0 | 4 |
_a006.3 _223 |
| 100 | 1 |
_aLin, Jimmy. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980639 |
|
| 245 | 1 | 0 |
_aData-Intensive Text Processing with MapReduce _h[electronic resource] / _cby Jimmy Lin, Chris Dyer. |
| 250 | _a1st ed. 2010. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2010. |
|
| 300 |
_aIX, 171 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_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 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. | ||
| 650 | 0 |
_aArtificial intelligence. _93407 |
|
| 650 | 0 |
_aNatural language processing (Computer science). _94741 |
|
| 650 | 0 |
_aComputational linguistics. _96146 |
|
| 650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aNatural Language Processing (NLP). _931587 |
| 650 | 2 | 4 |
_aComputational Linguistics. _96146 |
| 700 | 1 |
_aDyer, Chris. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _980640 |
|
| 710 | 2 |
_aSpringerLink (Online service) _980641 |
|
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031010088 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783031032646 |
| 830 | 0 |
_aSynthesis Lectures on Human Language Technologies, _x1947-4059 _980642 |
|
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-02136-7 |
| 912 | _aZDB-2-SXSC | ||
| 942 | _cEBK | ||
| 999 |
_c85001 _d85001 |
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