Tutorial on Formal Methods for Event Processing
European Conference on Artificial Intelligence (ECAI), 2014
Organisations require techniques for automated transformation of the Big Data they collect into operational knowledge. This requirement may be addressed by employing event processing systems that detect activities/events of special significance within an organisation, given streams of low-level information that are very difficult to be utilised by humans. We review a Chronicle Recognition System (CRS), the Event Calculus (EC), ProbLog and Markov Logic Networks (MLN). CRS is a purely temporal reasoning system that allows for efficient event processing. EC allows for the representation of temporal and atemporal constraints. Consequently, EC may be used in applications requiring spatial reasoning, for example. ProbLog and MLN, unlike EC and CRS, allow for uncertainty representation and are thus suitable for event processing in noisy environments. The manual development of event structures is a tedious, time-consuming and error-prone process. Moreover, it is often necessary to update such structures during the event recognition process, due to new information about the application under consideration. For this reason, we will review machine learning techniques automating the construction and refinement of event definitions.