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 and the cost of expressive inferences. Strider-lsa proposes such a trade-off and combines a scalable RDF stream processing engine with an efficient reasoning system. The main reasoning tasks are based on a query rewriting approach for SPARQL that benefits from an intelligent encoding of RDFS+ (RDFS + owl:sameAs) ontology elements. Strider-lsa 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.
@article{arxiv.1708.06521,
title = {Strider-lsa: Massive RDF Stream Reasoning in the Cloud},
author = {Xiangnan Ren and Olivier Curé and Hubert Naacke and Li Ke},
journal= {arXiv preprint arXiv:1708.06521},
year = {2017}
}