Abstract
This paper presents a novel approach, QSMat, for efficient RDF data stream querying with flexible query-based materialization. Previous work accelerates either 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 shows that QSMat can significantly reduce window materialization size, reasoning overhead, and thus stream query evaluation time.
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Notes
- 1.
Github: https://github.com/cmth/qsmat.
- 2.
Otherwise the stream would have to contain or imply schematic information that was specified as cut in the rule-set, which is a design error.
- 3.
That is why rules that never produced any restricted live-pattern during static search are not needed to satisfy the query, hence are safely removed by QSMat.
- 4.
As a pathological counterexample take a query with the where clause (?s ?p ?o): since the query matches all derivable triples, none can be excluded.
- 5.
To give an example: Assume a query matches (?x rdf:type A), and A has subclasses B and C. If no other way to derive membership in A, B or C exists, then it is sufficient to find all (?x rdf:type A), (?x rdf:type B) and (?x rdf:type C).
References
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Acknowledgments
This research was partially supported by the German Federal Ministry for Education and Research (BMB+F) in the project INVERSIV and the European Commission in the project CREMA.
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Mathieu, C., Klusch, M., Glimm, B. (2017). QSMat: Query-Based Materialization for Efficient RDF Stream Processing. In: Różewski, P., Lange, C. (eds) Knowledge Engineering and Semantic Web. KESW 2017. Communications in Computer and Information Science, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-69548-8_12
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DOI: https://doi.org/10.1007/978-3-319-69548-8_12
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