Towards Efficient Schema-Enhanced Pattern Matching over RDF Data Streams

Data streams, often seen as sources of events, have appeared on the Web. Event processing on theWeb needs however to cope with the typical openness and heterogeneity of the Web environment. Semantic Web technology, meant to facilitate data integration in an open environment, can help to address heterogeneities across multiple streams. In this paper we discuss an approach towards efficient pattern matching over RDF data streams based on the Rete algorithm, which can be considered as a rst building block for event processing on the Web. Our approach focuses on enhancing Rete with knowledge from the RDF schema associated with data streams, so that implicit knowledge can contribute to pattern matching. Moreover, we cover Rete extensions that cope with the streaming nature of the processed data, such as support for temporal operators, time windows, consumption strategies and garbage collection.