Graham Cormode

Small Summaries for Big Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 12. November 2020
• Englisch
• ISBN-13: 9781108805582
• Artikelnr.: 66183605

Autorenporträt

Graham Cormode is a Professor in Computer Science at the University of Warwick, doing research in data management, privacy and big data analysis. Previously, he was a principal member of technical staff at AT&T Labs-Research. His work has attracted more than 14,000 citations and has appeared in more than 100 conference papers, 40 journal papers, and been awarded 30 US Patents. Cormode is the co-recipient of the 2017 Adams Prize for Mathematics for his work on Statistical Analysis of Big Data. He has edited two books on applications of algorithms and co-authored a third.

Inhaltsangabe

1. 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.