Data-Intensive Text Processing with MapReduce (Record no. 85001)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03628nam a22005295i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-02136-7 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730163820.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2010 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031021367 |
| -- | 978-3-031-02136-7 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Lin, Jimmy. |
| 245 10 - TITLE STATEMENT | |
| Title | Data-Intensive Text Processing with MapReduce |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2010. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | IX, 171 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Human Language Technologies, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- MapReduce Basics -- MapReduce Algorithm Design -- Inverted Indexing for Text Retrieval -- Graph Algorithms -- EM Algorithms for Text Processing -- Closing Remarks. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Dyer, Chris. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02136-7 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2010. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural language processing (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational linguistics. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural Language Processing (NLP). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Linguistics. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1947-4059 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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