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2013 IEEE 29th International Conference on Data Engineering (ICDE) (2002)
San Jose, California
Feb. 26, 2002 to Mar. 1, 2002
ISBN: 0-7695-1531-2
pp: 0129
Pradeep Shenoy , Univ. of Washington-Seattle
Raghav Kaushik , Univ. of Wisconsin-Madison
Ehud Gudes , Ben-Gurion Univ.
Philip Bohannon , Bell Labs
XML and other semi-structured data may have partially specified or missing schema information, motivating the use of a structural summary which can be automatically computed from the data. These summaries also serve as indices for evaluating the complex path expressions common to XML and semi-structured query languages. However, to answer all path queries accurately, summaries must encode information about long, seldom-queried paths, leading to increased size and complexity with little added value. We introduce the A(k)-indices, a family of approximate structural summaries. They are based on the concept of k-bisimilarity, in which nodes are grouped based on local structure, i.e., the incoming paths of length up to k. The parameter k thus smoothly varies the level of detail (and accuracy) of the A(k)-index. For small values of k, the size of the index is substantially reduced. While smaller, the A(k) index is approximate, and we describe techniques for efficiently extracting exact answers to regular path queries. Our experiments show that, for moderate values of k, path evaluation using the A(k)-index ranges from being very efficient for simple queries to competitive for most complex queries, while using significantly less space than comparable structures.
Pradeep Shenoy, Raghav Kaushik, Ehud Gudes, Philip Bohannon, "Exploiting Local Similarity for Indexing Paths in Graph-Structured Data", 2013 IEEE 29th International Conference on Data Engineering (ICDE), vol. 00, no. , pp. 0129, 2002, doi:10.1109/ICDE.2002.994703
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