Counting graph matches with adaptive statistics collection

Abstract

High performance of query processing in large scale graph-structured data poses a pressing demand for high-quality statistics collection and selectivity estimation. Precise and succinct statistics collection about graph-structured data plays a crucial role for graph query selectivity estimation. In this paper, we propose the approach SMT, Succinct Markov Table, which achieves high precision in selectivity estimation with low memory space consumed. Four core notions of SMT are constructing, refining, compressing and estimating. The efficient algorithm SMTBuilder provides facility to build adaptive statistics model in the form of SMT. Versatile optimization rules, which investigate local bi-directional reachability, are introduced in SMT refining. During compressing, affective SMT grouping techniques are introduced. Statistical methods are used for selectivity estimations of various graph queries basing on SMT, especially for twig queries. By a thorough experimental study, we demonstrate SMT's advantages in accuracy and space by comparing with previously known alternative, as well as the preferred optimization rules and compressing technique that would favor different real-life data. © Springer-Verlag Berlin Heidelberg 2006.