Below you may find the slides and the exercises of the course on Complex Event Recognition. Once you submit your answers to the exercises, please email Alexander Artikis for the results.

Presenters:

Alexander Artikis, Periklis Mantenoglou

Abstract:

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 of 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. In this course, we will present formal methods for CER, and illustrate them using the domain of maritime situational awareness.

Slides:

Exercises:

Once you submit your answers to the exercises, please email Alexander Artikis for the results.

Exercises on the introductory material for Complex Event Recognition

Exercises on the Run-Time Event Calculus

Exercises on Probabilistic Complex Event Recognition

Exercises on Complex Event Forecasting

Webpage:

5th ACM Europe Summer School on Data Science