
Sublinear Algorithms for Big Data Applications
Published on 20. August 2015
Book
Paperback/Softback
XI, 85 pages
978-3-319-20447-5 (ISBN)
Description
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.
Reviews / Votes
"Wang and Han's book focuses on sublinear algorithms for processing big data. . For the researcher, this book also shows that there is room for improvement and new discoveries in this flourishing area. . The book is thus recommended mainly to researchers, but just as a piece of the bigger puzzle of sublinear algorithms for big data processing and applications." (Corrado Mencar, Computing Reviews, computingreviews.com, August, 2016)
More details
Series
Edition
1st ed. 2015
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
10 s/w Abbildungen, 20 farbige Abbildungen
XI, 85 p. 30 illus., 20 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 6 mm
Weight
166 gr
ISBN-13
978-3-319-20447-5 (9783319204475)
DOI
10.1007/978-3-319-20448-2
Schweitzer Classification
Other editions
Additional editions

Dan Wang | Zhu Han
Sublinear Algorithms for Big Data Applications
E-Book
07/2015
Springer
€53.49
Available for download
Persons
Content
Introduction.- Basics for Sublinear Algorithms.- Applications for Wireless Sensor Networks.- Applications for Big Data Processing.- Applications for a Smart Grid.- Concluding Remarks.