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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 637-648
Ke Zhu , University of New South Wales, Australia
Xuemin Lin , University of New South Wales, Australia
Ying Zhang , University of New South Wales, Australia
Haichuan Shang , University of New South Wales, Australia
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 bottom-up 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.
CITATION
Ke Zhu, Xuemin Lin, Ying Zhang, Haichuan Shang, "Similarity search on supergraph containment", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 637-648, doi:10.1109/ICDE.2010.5447846
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