Reactive Reasoning with the Event Calculus
This work addresses the need for efficient and scalable symbolic event recognition systems in domains with variable data delays and revisions, though it appears incremental as it builds on existing Event Calculus methods.
The paper tackles the problem of real-time event recognition from delayed and revised data streams by introducing RTEC, an Event Calculus dialect with novel implementation and windowing techniques, achieving scalability and meeting performance requirements for real-time applications.
Systems for symbolic event recognition accept as input a stream of time-stamped events from sensors and other computational devices, and seek to identify high-level composite events, collections of events that satisfy some pattern. RTEC is an Event Calculus dialect with novel implementation and 'windowing' techniques that allow for efficient event recognition, scalable to large data streams. RTEC can deal with applications where event data arrive with a (variable) delay from, and are revised by, the underlying sources. RTEC can update already recognised events and recognise new events when data arrive with a delay or following data revision. Our evaluation shows that RTEC can support real-time event recognition and is capable of meeting the performance requirements identified in a recent survey of event processing use cases.