Department of Computer Science
Selectivity estimation of twig queries on cyclic graphs
Recent applications including the Semantic Web, Web ontology and XML have sparked a renewed interest on graph-structured databases. Among others, twig queries have been a popular tool for retrieving subgraphs from graph-structured databases. To optimize twig queries, selectivity estimation has been a crucial and classical step. However, the majority of existing works on selectivity estimation focuses on relational and tree data. In this paper, we investigate selectivity estimation of twig queries on possibly cyclic graph data. To facilitate selectivity estimation on cyclic graphs, we propose a matrix representation of graphs derived from prime labeling a scheme for reachability queries on directed acyclic graphs. With this representation, we exploit the consecutive ones property (C1P) of matrices. As a consequence, a node is mapped to a point in a two-dimensional space whereas a query is mapped to multiple points. We adopt histograms for scalable selectivity estimation. We perform an extensive experimental evaluation on the proposed technique and show that our technique controls the estimation error under 1.3% on XMARK and DBLP, which is more accurate than previous techniques. On TREEBANK, we produce RMSE and NRMSE 6.8 times smaller than previous techniques. © 2011 IEEE.
Source Publication Title
The 2011 IEEE 27th International Conference on Data Engineering
Link to Publisher's Edition
Peng, Y., Choi, B., & Xu, J. (2011). Selectivity estimation of twig queries on cyclic graphs. The 2011 IEEE 27th International Conference on Data Engineering, 960-971. https://doi.org/10.1109/ICDE.2011.5767893