Retractable Complex Event Processing and Stream Reasoning

Complex Event Processing (CEP) deals with processing of continuously arriving events with the goal of identifying meaningful patterns (complex events). In existing stream database approaches, CEP is manly concerned by temporal relations between events. This paper advocates for a knowledge-rich CEP with Stream Reasoning capabilities. Secondly, we address the problem of revision in event processing. Events are often assumed to be immutable and therefore always correct. Revision in event processing deals with the circumstance that certain events may be revoked. This necessitates to reconsider complex events which might have been computed based on the original, flawy history as soon as part of that history is corrected. In this paper, we present a novel approach for knowledge-based CEP and Stream Reasoning, including revisions of events too. We present a rule-based language for pattern matching over event streams with a precise syntax and the declarative semantics. We devise an execution model for the proposed formalism, and provide a prototype implementation. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approach.