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: | 9783319204475 |
| Publication date: | 20th August 2015 |
| Author: | Dan Wang, Zhu Han |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Paperback |
| Pagination: | 85 pages |
| Series: | SpringerBriefs in Computer Science |
| Genres: |
Databases Communications engineering / telecommunications Network hardware |
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.
Sublinear Algorithms for Big Data Applications features in the following genres: Databases, Communications engineering / telecommunications, Network hardware
Paperback. Not Available.
Sublinear Algorithms for Big Data Applications was written by Dan Wang, Zhu Han and published by Springer an imprint of Springer International Publishing
Sublinear Algorithms for Big Data Applications has 85 pages
Yes it is part of SpringerBriefs in Computer Science series