Small Summaries for Big Data by Graham Cormode (English) Hardcover Book

Description: Small Summaries for Big Data by Graham Cormode, Ke Yi The 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Author Biography 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. Ke Yi is a Professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He obtained his PhD from Duke University. His research spans theoretical computer science and database systems. He has received the SIGMOD Best Paper Award (2016), a SIGMOD Best Demonstration Award (2015), and a Google Faculty Research Award (2010). He currently serves as an Associate Editor of ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. Table of Contents 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. Review A 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 Promotional A comprehensive introduction to flexible, efficient tools for describing massive data sets to improve the scalability of data analysis. Review Quote A 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 Promotional "Headline" A comprehensive introduction to flexible, efficient tools for describing massive data sets to improve the scalability of data analysis. Description for Bookstore The 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. Description for Library The 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. Details ISBN1108477445 Year 2020 ISBN-10 1108477445 ISBN-13 9781108477444 Format Hardcover Language English Author Ke Yi Publisher Cambridge University Press Publication Date 2020-11-12 DEWEY 005.7 UK Release Date 2020-11-12 Imprint Cambridge University Press Place of Publication Cambridge Country of Publication United Kingdom AU Release Date 2020-11-12 NZ Release Date 2020-11-12 Illustrations Worked examples or Exercises Pages 278 Alternative 9781108769938 Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:168645711;

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