Sparkwave: continuous schema-enhanced pattern matching over RDF data streams

Abstract

Data streams, often seen as sources of events, have appeared on the Web. Stream processing on the Web needs however to cope with the typical openness and heterogeneity of the Web environment. Semantic Web technologies, meant to facilitate data integration in an open environment, can help to address heterogeneities across multiple streams. In this paper we present Sparkwave, an approach for continuous pattern matching over RDF data streams. Sparkwave is based on the Rete algorithm, which allows efficient and truly continuous processing of data streams. Sparkwave is able to leverage RDF schema information associated to data streams to compute entailments, so that implicit knowledge is taken into account for pattern matching. In addition, it further extends Rete to support time-based sliding windows and static data instances, to cope with the streaming nature of processed data and real-world use cases.