Large data algorithmics
- 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.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Department of Electrical Engineering
|Statement of responsibility
|Submitted to the Department of Electrical Engineering.
|Thesis (Ph.D.)--Stanford University, 2012.
- © 2012 by Bahman Bahmani
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...