Dbpedia sparql benchmark – performance assessment with real queries on real data

Abstract

Abstract. Triple stores are the backbone of increasingly many Data Web appli-cations. It is thus evident that the performance of those stores is mission critical for individual projects as well as for data integration on the Data Web in gen-eral. Consequently, it is of central importance during the implementation of any of these applications to have a clear picture of the weaknesses and strengths of current triple store implementations. In this paper, we propose a generic SPARQL benchmark creation procedure, which we apply to the DBpedia knowledge base. Previous approaches often compared relational and triple stores and, thus, settled on measuring performance against a relational database which had been con-verted to RDF by using SQL-like queries. In contrast to those approaches, our benchmark is based on queries that were actually issued by humans and applica-tions against existing RDF data not resembling a relational schema. Our generic procedure for benchmark creation is based on query-log mining, clustering and SPARQL feature analysis. We argue that a pure SPARQL benchmark is more use-ful to compare existing triple stores and provide results for the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM. The subsequent comparison of our results with other benchmark results indicates that the per-formance of triple stores is by far less homogeneous than suggested by previous benchmarks. 1