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Algorithms for big data / by Moran Feldman

Publisher	Singapore : World Scientific
Year	2020
Authors	*Feldman, Moran

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Links to the text	Location	Volume	Call No.	Barcode No.	Status	Comments	ISBN	Printed	Restriction	Reserve
Links to the text	Library Off-campus access			OB00158296		World Scientific eBooks (電子ブック)	9789811204746

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Material Type	E-Book
Media type	機械可読データファイル
Size	1 online resource (x, 447 p.)
Notes	Mode of access: World Wide Web System requirements: Adobe Acrobat Reader Includes index 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"--Publisher's website Preface -- About the author -- Data stream algorithms. Introduction to data stream algorithms. Basic probability and tail bounds. Estimation algorithms. Reservoir sampling. Pairwise independent hashing. Counting distinct tokens. Sketches. Graph data stream algorithms. The sliding window model -- Sublinear time algorithms. Introduction to sublinear time algorithms. Property testing. Algorithms for bounded degree graphs. An algorithm for dense graphs. Algorithms for boolean functions -- Map-reduce. Introduction to map-reduce. Algorithms for lists. Graph algorithms. Locality-sensitive hashing -- Index HTTP:URL=https://www.worldscientific.com/worldscibooks/10.1142/11398#t=toc Pub. note=Access to full text is restricted to subscribers.
Subjects	LCSH:Algorithms LCSH:Electronic books
Classification	LCC:QA9.58 DC23:005.7015181
ID	8000078287
ISBN	9789811204746