大葉大學圖書館 |

Algorithms for big data[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	005.7015181
書名/作者:	Algorithms for big data/ Moran Feldman.
作者:	Feldman, Moran.
出版者:	Singapore : : World Scientific,, c2020.
面頁冊數:	1 online resource (x, 447 p.)
附註:	Includes index.
標題:	Algorithms.
ISBN:	9789811204746
ISBN:	9789811204753
內容註:	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.
摘要、提要註:	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.
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/11398#t=toc

Algorithms for big data[electronic resource] /
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:

182797
Algorithms.

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

Dewey Class. No.: 005.7015181

Algorithms for big data[electronic resource] /
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020 $a 9789811204753 $q (ebook)
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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 182797
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/11398#t=toc