Estimating selectivity for joined RDF triple patterns

A fundamental problem related to RDF query processing is selectivity estimation, which is crucial to query optimization for determining a join order of RDF triple patterns. In this paper we focus research on selectivity estimation for SPARQL graph patterns. The previous work takes the join uniformity assumption when estimating the joined triple patterns. This assumption would lead to highly inaccurate estimations in the cases where properties in SPARQL graph patterns are correlated. We take into account the dependencies among properties in SPARQL graph patterns and propose a more accurate estimation model. Since star and chain query patterns are common in SPARQL graph patterns, we first focus on these two basic patterns and propose to use Bayesian network and chain histogram respectively for estimating the selectivity of them. Then, for estimating the selectivity of an arbitrary SPARQL graph pattern, we design algorithms for maximally using the precomputed statistics of the star path...

• Type: Journal
• Year: 2011
• Where: CIKM
• Authors: Hai Huang 0003, Chengfei Liu
• Keywords: Accurate Estimation, CIKM 2011, Estimation Model, Information Technology, Selectivity Estimation