MapReduce进行密集文本数据处理 Data-Intensive Text Processing with MapReduce 文字版[PDF]

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资源信息： 中文名: MapReduce进行密集文本数据处理 原名: Data-Intensive Text Processing with MapReduce 作者: Jimmy Lin Chris Dyer 图书分类: 软件 资源格式: PDF 版本: 文字版 出版社: SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGIES 书号: 9781608453436 发行时间: 2010年 地区: 美国 语言: 英文 概述:

内容介绍： 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.　 内容截图　

目录: 1.Introduction 2.MapReduce Basics 3.MapReduce algorithm design 4.Inverted Indexing for Text Retrieval 5.Graph Algorithms 6.EM Algorithms for Text Processing 7.Closing Remarks