  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.
