SCHEDULE: NOV 16-22, 2013
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Efficient Data Partitioning Model for Heterogeneous Graphs in the Cloud
SESSION: Data Management in the Cloud
EVENT TYPE: Papers
TIME: 10:30AM - 11:00AM
SESSION CHAIR: Erwin Laure
AUTHOR(S):Kisung Lee, Ling Liu
As the size and variety of information networks continue to grow in many scientific domains, we witness a growing demand for efficient processing of large heterogeneous graphs using a cluster of compute nodes in the Cloud. One of the main open issues is how to effectively partition a large graph to process complex graph operations efficiently. In this paper, we present a distributed data partitioning framework for efficient processing of large-scale graphs in the Cloud. First, we introduce extended vertex blocks as graph partitioning building blocks. Second, we propose vertex block grouping algorithms which group those vertex blocks that have high correlation in graph structure to the same partition. Third, we propose a partition-guided query partitioning model which transforms graph queries into vertex block-based graph query patterns for parallel processing of graph queries. We conduct extensive experiments on several real-world graphs to show the effectiveness and scalability of our framework.
Erwin Laure (Chair) - KTH Royal Institute of Technology
Kisung Lee - Georgia Institute of Technology
Ling Liu - Georgia Institute of Technology
The full paper can be found in the ACM Digital Library