QSMat: Query-Based Materialization for Efficient RDF Stream Processing

Christian Mathieu; Matthias Klusch; Birte Glimm

In: 8th International Conference on Knowledge Engineering and Semantic Web. Knowledge Engineering and Semantic Web (KESW-17), November 8-10, Stettin, Poland, LNCS, Springer, 2017.


This paper presents a novel approach, QSMat, for effcient RDF data stream querying with flexible query-based materialization. Previous work either accelerates the maintenance of a stream window materialization or the evaluation of a query over the stream. QSMat exploits knowledge of a given query and entailment rule-set to accelerate window materialization by avoiding inferences that provably do not affect the evaluation of the query. We prove that stream querying over the resulting partial window materializations with QSMat is sound and complete with regard to the query. A comparative experimental performance evaluation based on the Berlin SPARQL benchmark and with selected representative systems for stream reasoning show that QSMat can signi cantly reduce window materialization size, reasoning overhead, and thus stream query evaluation time.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence