Sublinear Algorithms for Big Data Applications / by Dan Wang, Zhu Han.

Format:

Book

Author/Creator:

Wang, Daniel E., author.

Han, Zhu, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

SpringerBriefs in computer science 2191-5768

SpringerBriefs in Computer Science, 2191-5768

Language:

English

Subjects (All):

Database management.

Computer networks.

Electrical engineering.

Database Management.

Computer Communication Networks.

Communications Engineering, Networks.

Local Subjects:

Database Management.

Computer Communication Networks.

Communications Engineering, Networks.

Physical Description:

1 online resource (XI, 85 pages) : 30 illustrations, 20 illustrations in color.

Edition:

First edition 2015.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2015.

System Details:

text file PDF

Summary:

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.

Contents:

Introduction

Basics for Sublinear Algorithms

Applications for Wireless Sensor Networks

Applications for Big Data Processing

Applications for a Smart Grid

Concluding Remarks.

Other Format:

Printed edition:

ISBN:

978-3-319-20448-2

9783319204482

Access Restriction:

Restricted for use by site license.