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feldman m algorithms big data 2020

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Textbook in PDF format 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. Preface About the Author Data Stream Algorithms Introduction to Data Stream Algorithms Chapter 2. Basic Probability and Tail Bounds Estimation Algorithms Reservoir Sampling Pairwise Independent Hashing Counting Distinct Tokens Sketches Graph Data Stream Algorithms The Sliding Window Model Sublinear Time Algorithms Introduction to Sublinear Time Algorithms Property Testing Algorithms for Bounded Degree Graphs An Algorithm for Dense Graphs Algorithms for Boolean Functions Map-Reduce Introduction to Map-Reduce Algorithms for Lists Graph Algorithms Locality-Sensitive Hashing Index

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Feldman M. Algorithms For Big Data 2020.pdf 19.4 MB

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feldman m algorithms big data 2020