文件名称:
Data-Intensive Text Processing with MapReduce Jimmy Lin and Chris Dyer
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上传时间: 2012-06-13
详细说明： Contents 1 Introduction 1.1 Computing in the Clouds 1.2 Big Ideas 1.3 Why Is This Dierent? 1.4 What This Book Is Not 2 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 3 MapReduce Algorithm Design 3.1 Local Aggregation 3.1.1 Combiners and In-Mapper Combining 3.1.2 Algorithmic Correctness with Local Aggregation 3.2 Pairs and Stripes 3.3 Computing Relative Frequen cies 3.4 Secondary Sorting 3.5 Relational Joins 3.5.1 Reduce-Side Join 64 3.5.2 Map-Side Join 66 3.5.3 Memory-Backed Join 67 3.6 Summary 4 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 80 4.5.2 Bit-Aligned Codes 82 4.5.3 Postings Compression 84 4.6 What About Retrieval? 4.7 Summary and Additional Readings 5 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 6 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 133 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 6.4.2 Brief Digression: Language Modeling with MapReduce 6.4.3 Word Alignment 6.4.4 Experiments 6.5 EM-Like Algorithms 6.5.1 Gradient-Based Optimization and Log-Linear Models 6.6 Summary and Additional Readings 7 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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