Name
Abstract | ||
|---|---|---|
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 u0027windowingu0027 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. |
Year | Venue | DocType |
|---|---|---|
2015 | arXiv: Artificial Intelligence | Journal |
Volume | Citations | PageRank |
abs/1505.05364 | 1 | 0.36 |
References | Authors | |
18 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Artikis | 1 | 1142 | 82.51 |
| Marek Sergot | 2 | 549 | 56.98 |
| Georgios Paliouras | 3 | 1510 | 120.93 |