國立虎尾科技大學 |

Algorithms for big data

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Algorithms for big data/ Moran Feldman.
Author:	Feldman, Moran.
Published:	Singapore :World Scientific, : c2020.,
Description:	1 online resource (x, 447 p.)
Notes:	Includes index.
Subject:	Algorithms. -
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/11398#t=toc
ISBN:	9789811204746

Algorithms for big data
Feldman, Moran.

Algorithms for big data[electronic resource] /Moran Feldman. - 1st ed. - Singapore :World Scientific,c2020. - 1 online resource (x, 447 p.)

Includes index.

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.

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.

Mode of access: World Wide Web.

ISBN: 9789811204746Subjects--Topical Terms:

527865
Algorithms.

LC Class. No.: QA9.58 / .F45 2020

Dewey Class. No.: 005.7015181

Algorithms for big data
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050 0 4 $a QA9.58 $b .F45 2020
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100 1 $a Feldman, Moran. $3 1342560
245 1 0 $a Algorithms for big data $h [electronic resource] / $c Moran Feldman.
250 $a 1st ed.
260 $a Singapore : $b World Scientific, $c c2020.
300 $a 1 online resource (x, 447 p.)
500 $a Includes index.
505 0 $a 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.
520 $a 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.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Description based on print version record.
650 0 $a Algorithms. $3 527865
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/11398#t=toc