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Sublinear algorithms for big data ap...

Han, Zhu.

Sublinear algorithms for big data applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sublinear algorithms for big data applicationsby Dan Wang, Zhu Han.
Author:	Wang, Dan.
other author:	Han, Zhu.
Published:	Cham :Springer International Publishing :2015.
Description:	xi, 85 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Computer algorithms.
Online resource:	http://dx.doi.org/10.1007/978-3-319-20448-2
ISBN:	9783319204482 (electronic bk.)

Sublinear algorithms for big data applications
Wang, Dan.

Sublinear algorithms for big data applications[electronic resource] /by Dan Wang, Zhu Han. - Cham :Springer International Publishing :2015. - xi, 85 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Introduction -- Basics for Sublinear Algorithms -- Applications for Wireless Sensor Networks -- Applications for Big Data Processing -- Applications for a Smart Grid -- Concluding Remarks.

The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.

ISBN: 9783319204482 (electronic bk.)

Standard No.: 10.1007/978-3-319-20448-2doiSubjects--Topical Terms:

184478
Computer algorithms.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.1

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520 $a The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.
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