User-Configurable Semantic Data Stream Reasoning Using SPARQL Update

Stream reasoning is one of the building blocks giving semantic web an advantage in the race for the real-time web. This paper demonstrates implementation of materialisation-based reasoning using an event processor supporting networks of specification-compliant SPARQL Update rules. Collections of rules coded in SPARQL leave the rule implementation exposed for selection and modification by the platform user using the same query language for both the queries and entailment rules. Observations on the differences of SPARQL and rule semantics are made. The entailment-category tests of the SPARQL 1.1 conformance test set are thoroughly reviewed. New rules are constructed to improve platform pass rate, and the test results are measured. An event-based memory handling solution to the accumulation of data in stream processing scenarios through separation of static data (e.g. the ontology) from dynamic event data is presented and tested. This implementation extends the reasoning support available in an RDF stream processor from RDF(S) to ρdf , D*, P-entailment and OWL 2 RL. The performance of the Instans platform is measured using a well-known benchmark requiring reasoning, comparing complete sets of entailment rules against the necessary subset to complete each test. Performance is also compared to non-streaming SPARQL query processors with reasoning support.Peer reviewe