Abstract

We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be retracted/revised by the underlying sources. We propose RTECinc, an incremental version of RTEC, a composite event recognition engine with a formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival and retraction of events by computing at each query time composite event intervals from scratch. This often results to redundant computations. Instead, RTECinc deals with delays and retractions in a more efficient way, by updating only the affected events. We evaluate RTECinc theoretically, presenting a complexity analysis, and show the conditions in which it outperforms RTEC. Moreover, we compare RTECinc and RTEC experimentally using two real-world datasets. The results are compatible with our theoretical analysis and show that RTECinc may outperform RTEC.