Towards Complex Event Processing in Linked Data Stream

The increasing popularity of Linked Data Streams has led to the developments of several RDF stream processing engines. Among them, CQELS has been developed with a native and adaptive approach, which gives it a performance advantage over other engines. In the traditional Complex Event Processing, temporal logics between events have a wide range of applications in the financial securities, traffic control, security control, health and other fields. However, existing RDF stream processing provides insufficient support for temporal logics and complex event detection. In some previous work, researchers have elaborated on the need for extending CQELS in the temporal logic and provided some theoretical ideas but failed to realize. In this paper, we extend the current version of CQELS with the support for temporal logics. Our implementation extends the CQELS language expressiveness and realizes complex event processing over linked data streams. Moreover, we demonstrate the efficiency of our approach with experimental results comparing to the original CQELS implementation.