ROSIE: Runtime Optimization of SPARQL Queries over RDF Using Incremental Evaluation

Abstract

RDF (Resource Description Framework) is a proposed standard for knowledge representation, with relational databases wildly adopted in RDF data management. For efficient evaluation of SPARQL queries over RDF data, the legacy query optimizer needs reconsiderations. One vital problem is how to tackle the suboptimal query plan caused by error-prone cardinality estimation. For RDF data, determine an optimal execution order before the query actually evaluated is costly, or even infeasible. In this paper, we propose ROSIE, a Runtime Optimization framework that iteratively re-optimize SPARQL query plan according to the actual cardinality derived from Incremental partial query Evaluation. By introducing an approach for heuristic-based plan generation, as well as a mechanism to detect cardinality estimation error at runtime, ROSIE relieves the problem of biased cardinality propagation in an efficient way. Extensive experiments on real and benchmark data have shown that, compared to the state-of-the-arts, ROSIE consistently outperformed on complex queries by orders of magnitude.