09.02.20 - 14.02.20, Seminar 20071

Foundations of Composite Event Recognition

The following text appeared on our web pages prior to the seminar, and was included as part of the invitation.


Composite Event Recognition (CER for short), also known as Complex Event Recognition, 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 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 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 arrhythmia, epidemic spread, and the monitoring of cyber-physical systems. In each application, CER allows to make sense of streaming data, react accordingly, and prepare for counter-measures.

Numerous CER systems and languages have been proposed in the literature. While these systems have a common goal, they differ in their architectures, data models, pattern languages, and processing mechanisms, resulting in many heterogeneous implementations with sometimes fundamentally different capabilities. Their comparative assessment is further hindered by the fact that they have been developed in different communities, each bringing in their own terminology and view of the problem. Moreover, the established CER literature focuses on the practical system aspects of CER. As a result, little work has been done on its formal foundations. Consequently, and in contrast to the situation for more traditional fields in Computer Science, we currently lack a common understanding of the trade-offs between expressiveness and complexity in the design of CER systems, as well as an established theory for comparing their fundamental capabilities.

As such, currently, CER frameworks are difficult to understand, extend and generalize. It is unclear which of the proposed approaches better meets the requirements of a given application domain, in terms of capturing the intended meaning of the composite events of interest, as well as detecting them efficiently. Furthermore, the lack of foundations makes it hard to leverage established results --- from automata theory, temporal logics, etc --- thus hindering scientific and technological progress in CER.

At the same time, recent years have witnessed increased activity in diverse fields of Computer Science on topics that are related to CER:

  • inductive and deductive reasoning over streaming data, a field known as Stream Reasoning in Artificial Intelligence;
  • theoretical complexity results related to processing database queries under updates, associated with advances in Incremental View Maintenance in Database research;
  • expressiveness and complexity of logics in a dynamic setting, in Logic research.

Given this setting, the objective of this Dagstuhl Seminar is to:

  • bring together world-class computer scientists and practitioners working on CER, Distributed Systems, Databases, Logic, Stream Reasoning and Artificial Intelligence;
  • to disseminate the recent foundational results in each of these isolated fields among all participants;
  • to identify the open problems that need to be resolved to provide general formal foundations of CER;
  • to establish new research collaborations among these fields; thereby
  • to start making progress towards formulating such foundations.

Creative Commons BY 3.0 DE
Alexander Artikis, Thomas Eiter, Alessandro Margara, and Stijn Vansummeren