Year : 2008 SPARQL Basic Graph Pattern Optimization Using Selectivity

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 define and analyze the characteristics of heuristics for selectivity-based 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. Note that the SPARQL versions of the 14 LUBM queries and the University0 data set we used in this paper can be downloaded from here. SPARQL Basic Graph Pattern Optimization Using Selectivity Estimation Markus Stocker HP Laboratories Bristol United Kingdom markus.stocker@gmail.com Andy Seaborne HP Laboratories Bristol United Kingdom andy.seaborne@hp.com Abraham Bernstein Department of Informatics University of Zurich Switzerland bernstein@ifi.uzh.ch Christoph Kiefer Department of Informatics University of Zurich Switzerland kiefer@ifi.uzh.ch Dave Reynolds HP Laboratories Bristol United Kingdom dave.reynolds@hp.com