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Small Summaries for Big Data by Graham Cormode and Ke Yi (2020, Hardcover)

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About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101108477445

ISBN-139781108477444

eBay Product ID (ePID)5050093837

Product Key Features

Number of Pages278 Pages

LanguageEnglish

Publication NameSmall Summaries for Big Data

SubjectGeneral, Databases / General

Publication Year2020

TypeTextbook

AuthorGraham Cormode, Ke Yi

Subject AreaMathematics, Computers

FormatHardcover

Dimensions

Item Height0.7 in

Item Weight18 Oz

Item Length9.2 in

Item Width6.2 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2020-033065

Dewey Edition23

ReviewsA very thorough compendium of sketching and streaming algorithms, and an excellent resource for anyone interested in learning about them, understanding how they work and deploying them in applications. Good job! Piotr Indyk, Massachusetts Institute of Technology

IllustratedYes

Dewey Decimal005.7

Table Of Content1. Introduction; 2. Summaries for sets; 3. Summaries for multisets; 4. Summaries for ordered data; 5. Geometric summaries; 6. Graph summaries; 7. Vector, matrix and linear algebraic summaries; 8. Summaries over distributed data; 9. Other uses of summaries; 10. Lower bounds for summaries.

SynopsisThe massive volume of data generated in modern applications requires the ability to build compact summaries of datasets. This introduction aimed at students and practitioners covers algorithms to describe massive data sets from simple sums to advanced probabilistic structures, with applications in big data, data science, and machine learning., 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.

LC Classification NumberQA76.9.B45C67 2021