Strider-lsa: Massive RDF Stream Reasoning in the Cloud

TL;DR

Strider-lsa is a scalable cloud-based RDF stream reasoning system that balances throughput and inference complexity, enabling real-time anomaly detection in sensor data for water management.

Contribution

It introduces a novel trade-off approach combining scalable RDF stream processing with efficient reasoning using query rewriting and ontology encoding.

Findings

Runs in production at a water management company

Demonstrates high performance over multiple datasets

Balances data throughput with reasoning expressiveness

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 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.