The Taub Faculty of Computer Science Events and Talks

Complex event processing (CEP) is employed to detect user-specified patterns of events in data streams. CEP mechanisms operate by maintaining all sets of events that can potentially be composed into a pattern match. This approach can be wasteful when many of the sets do not participate in an actual match and are therefore discarded. We present DLACEP, a novel framework that fuses deep learning with CEP to efficiently extract complex pattern matches from streams. To the best of our knowledge, this is the first time deep learning is employed to detect events constituting a pattern match in the realm of CEP. To assess our approach, we performed extensive empirical testing on various scenarios with both synthetic and real-world data. We showcase examples in which our method achieves an increase in throughput of up to three orders of magnitude compared to solely employing CEP, while only suffering a minor loss in the number of detected matches.