Algorithms for Big Data

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.Contents: PrefaceAbout the AuthorData Stream Algorithms:Introduction to Data Stream AlgorithmsBasic Probability and Tail BoundsEstimation AlgorithmsReservoir SamplingPairwise Independent HashingCounting Distinct TokensSketchesGraph Data Stream AlgorithmsThe Sliding Window ModelSublinear Time Algorithms:Introduction to Sublinear Time AlgorithmsProperty TestingAlgorithms for Bounded Degree GraphsAn Algorithm for Dense GraphsAlgorithms for Boolean FunctionsMap-Reduce:Introduction to Map-ReduceAlgorithms for ListsGraph AlgorithmsLocality-Sensitive HashingIndex
Readership: Professionals, academics, researchers and graduate students in theoretical computer science and big data.Streaming Algorithms;Map-Reduce;Property Testing;Sublinear Time Algorithms00