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Tutorial at KR 2021: Complex Event Recognition and Forecasting

Complex Event Recognition (CER) refers to the activity of detecting patterns in streams of continuously arriving "event" data over (geographically) distributed sources. CER is a key ingredient of many contemporary Big Data applications that require the processing of such event streams in order to obtain timely insights and implement reactive and proactive measures. Examples of such applications include the recognition and forecasting of attacks in computer network nodes, human activities on video content, emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, error conditions in smart energy grids, violations of maritime regulations, cardiac arrhythmias and epidemic spread. In each application, CER allows to make sense of streaming data, react accordingly, and prepare for counter-measures.

We will present the formal methods for Complex Event Recognition (CER), i.e. models based on automata and computational logic, as they have been developed in the database, distributed systems, and artificial intelligence communities. For each of these models, we will present the reasoning algorithms that support on-line event recognition, as well as event forecasting, i.e. the computation of future intervals in which an event is likely to happen. To illustrate the reviewed approaches we will use a real-world use case from the INFORE project: complex event recognition for maritime situational awareness.