Remembering the Important Things: Semantic Importance in Stream Reasoning

Abstract

Reasoning and querying over data streams rely on the ability to deliver a sequence of stream snapshots to the processing algorithms. These snapshots are typically provided using windows as views into streams and associated window management strategies. In this work, we explore a general notion of \textit{semantic importance} that can be used for window management of RDF streaming data using semantically-aware processing algorithms. Semantic importance exploits the information in RDF streams and surrounding ontologies for ranking window data in terms of its contribution to solution mappings. We also consider how a stream window management strategy based on semantic importance could improve overall processing performance, especially as available window sizes decrease.