文件名称:
Data-Intensive Text Processing with MapReduce
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上传时间: 2011-03-27
详细说明： Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer University of Maryland, College Park Manuscript prepared April 11, 2010 ii Introduction 1 1.1 Computing in the Clouds 1.2 Big Ideas 1.3 Why Is This Different? 1.4 What This Book Is Not MapReduce Basics 2.1 Functional Programming Roots 2.2 Mappers and Reducers 2.3 The Execution Framework 2.4 Partitioners and Combiners 2.5 The Distributed File System 2.6 Hadoop Cluster Architecture 2.7 Summary MapReduce Algorithm Design 3.1 Local Aggregation 3.1.1 Combiner s and In-Mapper Combining 3.1.2 Algorithmic Correctness with Local Aggregation 3.2 Pairs and Stripes 3.3 Computing Relative Frequencies 3.4 Secondary Sorting 3.5 Relational Joins 3.5.1 Reduce-Side Join 3.5.2 Map-Side Join 3.5.3 Memory-Backed Join 3.6 Summary Inverted Indexing for Text Retrieval 4.1 Web Crawling 4.2 Inverted Indexes 4.3 Inverted Indexing: Baseline Implementation 4.4 Inverted Indexing: Revised Implementation 4.5 Index Compression 4.5.1 Byte-Aligned and Word-Aligned Codes 4.5.2 Bit-Aligned Codes 4.5.3 Postings Compression 4.6 What About Retrieval? 4.7 Summary and Additional Readings Graph Algorithms 5.1 Graph Representations 5.2 Parallel Breadth-First Search 5.3 PageRank 5.4 Issues with Graph Processing 5.5 Summary and Additional Readings EM Algorithms for Text Processing 6.1 Expectation Maximization 6.1.1 Maximum Likelihood Estimation 115 6.1.2 A Latent Variable Marble Game 117 6.1.3 MLE with Latent Variables 118 6.1.4 Expectation Maximization 119 6.1.5 An EM Example 120 6.2 Hidden Markov Models 6.2.1 Three Questions for Hidden Markov Models 123 6.2.2 The Forward Algorithm 125 6.2.3 The Viterbi Algorithm 126 6.2.4 Parameter Estimation for HMMs 129 6.2.5 Forward-Backward Training: Summary 6.3 EM in MapReduce 6.3.1 HMM Training in MapReduce 135 6.4 Case Study: Word Alignment for Statistical Machine Translation 6.4.1 Statistical Phrase-Based Translation 139 6.4.2 Brief Digression: Language Modeling with MapReduce 142 6.4.3 Word Alignment 143 6.4.4 Experiments 144 6.5 EM-Like Algorithms 6.5.1 Gradient-Based Optimization and Log-Linear Models 147 6.6 Summary and Additional Readings Closing Remarks 7.1 Limitations of MapReduce 7.2 Alternative Computing Paradigms 7.3 MapReduce and Beyond ...展开收缩
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