Event matching using semantic and spatial memories

Abstract

We address the problem of real-time matching and correlation of events which are detected and reported by humans. As in Twitter, facebook, blogs and phone calls, the stream of reported events are unstructured and require intensive manual processing. The plethora of events and their different types need a flexible model and a representation language that allows us to encode them for online processing. Current approaches in complex event processing and stream reasoning focus on temporal relationships between composite events and usually refer to pre-defined sensor locations. We propose a methodology and a computational framework for matching and correlating atomic and complex events which have no pre-defined schemas based on their content. Matching evaluation on real events show significant improvement compared to the manual matching process. © 2013 Springer-Verlag Berlin Heidelberg.