  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.
