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This unique volume is an introduction for computer scientists, including a formal study of theoretical algorithms for Big Data applications, which allows them to work on such algorithms in the future. It also serves as a useful reference guide for the general computer science population, providing a comprehensive overview of the fascinating world of such algorithms.To achieve these goals, the algorithmic results presented have been carefully chosen so that they demonstrate the important techniques and tools used in Big Data algorithms, and yet do not require tedious calculations or a very deep mathematical background.<b>Contents:</b> <ul><li>Preface</li><li>About the Author</li><li><b><i>Data Stream Algorithms:</i></b><ul><li>Introduction to Data Stream Algorithms</li><li>Basic Probability and Tail Bounds</li><li>Estimation Algorithms</li><li>Reservoir Sampling</li><li>Pairwise Independent Hashing</li><li>Counting Distinct Tokens</li><li>Sketches</li><li>Graph Data Stream Algorithms</li><li>The Sliding Window Model</li></ul></li><li><b><i>Sublinear Time Algorithms:</i></b><ul><li>Introduction to Sublinear Time Algorithms</li><li>Property Testing</li><li>Algorithms for Bounded Degree Graphs</li><li>An Algorithm for Dense Graphs</li><li>Algorithms for Boolean Functions</li></ul></li><li><b><i>Map-Reduce:</i></b><ul><li>Introduction to Map-Reduce</li><li>Algorithms for Lists</li><li>Graph Algorithms</li><li>Locality-Sensitive Hashing</li></ul></li><li>Index</li></ul><br><b>Readership:</b> Professionals, academics, researchers and graduate students in theoretical computer science and big data.Streaming Algorithms;Map-Reduce;Property Testing;Sublinear Time Algorithms00

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