Scalable RDF Stream Reasoning in the Cloud

Tracking #: 1747-2959

This paper is currently under review
Authors: 
Xiangnan Ren
OLIVIER CURE
Hubert Naacke
Ke Li

Responsible editor: 
Guest Editors Stream Reasoning 2017

Submission type: 
Full Paper
Abstract: 
Reasoning over semantically annotated data is an emerging trend in stream processing aiming to produce sound and complete answers to a set of continuous queries. It usually comes at the cost of finding a trade-off between data throughput, latency and the cost of expressive inferences. StriderR proposes such a trade-off and combines a scalable RDF stream processing engine with an efficient reasoning system. The main reasoning services are based on a query rewriting approach for SPARQL that benefits from an intelligent encoding of an extension of the RDFS (i.e., RDFS with owl:sameAs) ontology elements. StriderR runs in production at a major international water management company to detect anomalies from sensor streams. The system is evaluated along different dimensions and over multiple datasets to emphasize its performance.
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