Abstract-A supergraph containment search is to retrieve the data graphs contained by a query graph. In this paper, we study the problem of efficiently retrieving all data graphs approximately contained by a query graph, namely similarity search on supergraph containment. We propose a novel and efficient index to boost the efficiency of query processing. We have studied the query processing cost and propose two index construction strategies aimed at optimizing the performance of different types of data graphs: top-down strategy and bottomup strategy. Moreover, a novel indexing technique is proposed by effectively merging the indexes of individual data graphs; this not only reduces the index size but also further reduces the query processing time. We conduct extensive experiments on real data sets to demonstrate the efficiency and the effectiveness of our techniques.
Shang et al REF studied super-graph similarity search, and proposes top-down and bottom-up index construction strategy to optimize the performance of query processing.
Similarity search on supergraph containment
2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)
2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)
Computer Science