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Small Summaries for Big Data - Graham Cormode

Small Summaries for Big Data

By: Graham Cormode, Ke Yi

eText | 12 November 2020

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The massive volume of data generated in modern applications can overwhelm our ability to conveniently transmit, store, and index it. For many scenarios, building a compact summary of a dataset that is vastly smaller enables flexibility and efficiency in a range of queries over the data, in exchange for some approximation. This comprehensive introduction to data summarization, aimed at practitioners and students, showcases the algorithms, their behavior, and the mathematical underpinnings of their operation. The coverage starts with simple sums and approximate counts, building to more advanced probabilistic structures such as the Bloom Filter, distinct value summaries, sketches, and quantile summaries. Summaries are described for specific types of data, such as geometric data, graphs, and vectors and matrices. The authors offer detailed descriptions of and pseudocode for key algorithms that have been incorporated in systems from companies such as Google, Apple, Microsoft, Netflix and Twitter.

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Non-FictionComputing & I.T.DatabasesReference, Information & Interdisciplinary SubjectsResearch & InformationData AnalysisComputer Programming & Software DevelopmentAlgorithms & Data StructuresComputer ScienceDigital Signal Processing (DSP)Mathematical Theory of ComputationMathematics
Library & Info SciencesLibrary & Information Services

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