SPARQL Basic Graph Pattern Optimization Using Selectivity Estimation
In this paper, we formalize the problem of Basic Graph Pattern (BGP) optimization for SPARQL queries and main memory graph implementations of RDF data. We de.ne and analyze the characteristics of heuristics for selectivitybased static BGP optimization. The heuristics range from simple triple pattern variable counting to more sophisticated selectivity estimation techniques. Customized summary statistics for RDF data enable the selectivity estimation of joined triple patterns and the development of efficient heuristics. Using the Lehigh University Benchmark (LUBM), we evaluate the performance of the heuristics for the queries provided by the LUBM and discuss some of them in more details.
SPARQL query optimization selectivity estimation
Markus Stocker Andy Seaborne Abraham Bernstein Christoph Kiefer Dave Reynolds
HP Laboratories Bristol United Kingdom Department of Informatics University of Zurich Switzerland
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）