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Fast Graph Pattern Matching

Jiefeng Cheng, Jeffrey Xu Yu, Bolin Ding, Philip S. Yu, and Haixun Wang

Abstract

Due to rapid growth of the Internet technology and new scientific/technological advances, the number of applications that model data as graphs increases, because graphs have high expressive power to model complicated structures. The dominance of graphs in real-world applications asks for new graph data management so that users can access graph data effectively and efficiently. In this paper, we study a graph pattern matching problem over a large data graph. The problem is to find all patterns in a large data graph that match a user-given graph pattern. We propose a new two-step R-join (reachability join) algorithm with filter step and fetch step based on a cluster-based join-index with graph codes. We consider the filter step as an R-semijoin, and propose a new optimization approach by interleaving R-joins with R-semijoins. We conducted extensive performance studies, and confirm the efficiency of our proposed new approaches.

Details

Publication typeInproceedings
Published inProceedings of the 24th IEEE International Conference on Data Engineering (ICDE 2008)
Pages913-922
PublisherIEEE Computer Society
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