Towards efficient semantically enriched complex event processing and pattern matching
Syed Gillani, Gauthier Picard, Frédérique Laforest, Antoine Zimmermann · 2014
Abstract. Management and recognition of event patterns is becoming thoroughly ingrained in many application areas of Semantically enabled Complex Event Pro-cessing (SCEP). However, the reliance of state-of-the-art technologies on rela-tional and RDF triple model without having the notion of time has severe lim-itations. 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 cit-izen and should implement parallel and distributed processing to deal with large amount of data streams. In this paper, we discuss various challenges and limi-tations 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. 1