Incremental Reasoning for Fleet Management

We propose RTECinc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival of events by computing at each query time everything from scratch. This is often not efficient since it results to redundant computations. Instead, RTECinc deals with delays in a more efficient way, by updating only the affected events. We compare RTECinc and RTEC experimentally using a real-world dataset with position signals of vehicles.