*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Innovative Ming, Processing, and Application of Big Graphs
Yang Zhou
School of Computer Science
College of Computing
Georgia Institute of Technology
Date: Wednesday, October 12, 2016
Time: 3:00 PM - 5:00 PM EDT
Location: KACB 1315
Committee:
Dr. Ling Liu (Advisor, School of Computer Science, Georgia Institute of Technology)
Dr. Jay Lofstead (Scalable System Software Group, Sandia National Laboratories)
Dr. Shamkant Navathe (School of Computer Science, Georgia Institute of Technology)
Dr. Calton Pu (School of Computer Science, Georgia Institute of Technology)
Dr. Lakshmish Ramaswamy (Department of Computer Science, University of Georgia)
Abstract:
With continued advances in computing and information technology, big graphs have grown at an astonishing rate in terms of volume, variety, and velocity. Mining and processing such big graphs have huge potential to reveal hidden insights and promote innovation in many business, science, and engineering domains. This dissertation research is dedicated to the novel graph mining algorithms and the scalable graph processing frameworks.
This dissertation had made original contributions in graph mining, processing and application: First, we have developed a suite of novel graph mining algorithms to analyze and mine large-scale real-world heterogeneous information networks. Our algorithmic approaches enable new ways to dive into the correlation structure of big graphs to derive new insights about how heterogeneous entities interact with one another and influence the effectiveness and efficiency of graph clustering, graph classification and graph ranking. Second, we have developed a scalable graph parallel processing framework by exploring parallel processing optimizations at both access tier and computation tier. We have designed a suite of hierarchically composable graph parallel abstractions to enable large-scale graphs to be processed efficiently for iterative graph computation applications. Our approach enables computer hardware resource aware graph partitioning such that parallel graph processing workloads can be well balanced in the presence of highly irregular graph structures and the mismatch of graph access and computation workloads. Third but not the least, we have developed innovative domain specific graph analytics frameworks to understand the hidden patterns in enterprise storage systems and to derive the interesting correlations among various enterprise web services. These novel graph algorithms and frameworks provide broader and deeper insights for better understanding of tradeoffs in enterprise system design and implementation.
