Towards Efficient Semantically Enriched Complex Event Processing and Pattern Matching

Management and recognition of event patterns is becoming thoroughly ingrained in many application areas of Semantically enabled Complex Event Processing (SCEP). However, the reliance of state-of-the-art technologies on relational and RDF triple model without having the notion of time has severe limitations. This restricts the system to employ temporal reasoning at RDF level and use historical events to predict new situations. Additionally, the semantics of traditional query languages makes it quite challenging to implement distributed event processing. In our vision, SCEP needs to consider RDF as a first class citizen and should implement parallel and distributed processing to deal with large amount of data streams. In this paper, we discuss various challenges and limitations of state-of-the-art technologies and propose a possible solution to extend RDF data model for stream processing and pattern matching. Furthermore, we describe a high-level query design that enables efficient parallel and distributed pattern matching through query rewriting.