Sublinear Algorithms for Big Data Applications
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 a...
| Main Authors: | , |
|---|---|
| Corporate Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
|
| Series: | SpringerBriefs in Computer Science,
|
| Subjects: | |
| Online Access: | Full Text via HEAL-Link |
| 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. |
|---|---|
| Physical Description: | XI, 85 p. 30 illus., 20 illus. in color. online resource. |
| ISBN: | 9783319204482 |
| ISSN: | 2191-5768 |
