Large data algorithmics
Abstract/Contents
- Abstract
- In this thesis, we will explore the algorithmic aspect of large data applications on distributed frameworks. In the distributed batched processing setting, I will present highly scalable algorithms for the densest subgraph detection primitive in massive networks, as well as an efficient scalable algorithm called k-means [vertical line][vertical line] for the k-means clustering problem. In the distributed real-time processing setting, I will present algorithms for two important applications: incremental PageRank computation, and real-time social search.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2012 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Bahmani, Bahman |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering |
| Primary advisor | Goel, Ashish |
| Primary advisor | Raghavan, Prabhakar |
| Thesis advisor | Goel, Ashish |
| Thesis advisor | Raghavan, Prabhakar |
| Thesis advisor | Garcia-Molina, Hector |
| Advisor | Garcia-Molina, Hector |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Bahman Bahmani. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2012. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2012 by Bahman Bahmani
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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