A Tensor-Based Probabilistic Event Calculus

Complex Event Recognition (CER) systems receive as input a

stream of time-stamped events and identify situations of

interest that satisfy a given pattern. Streaming

environments are characterized by the high rate and volume

of input data, and thus, scalability is of crucial

importance. At the same time, noise and uncertainty are

ubiquitous in temporal data, and not considering them,

leads to erroneous detections. To confront these

challenges, we present a tensor-based formalization of the

Event Calculus (EC) for probabilistic inference, and

demonstrate the scalability of our approach with the use of

CER datasets from two real-world application domains.

Moreover, we demonstrate the benefits of our approach, in

terms of processing time, by comparing it against a

probabilistic logic programming implementation of EC.