09. – 14. Februar 2020, Dagstuhl-Seminar 20071

Foundations of Composite Event Recognition


Alexander Artikis (NCSR Demokritos – Athens, GR)
Thomas Eiter (TU Wien, AT)
Alessandro Margara (Polytechnic University of Milan, IT)
Stijn Vansummeren (Free University of Brussels, BE)

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Dagstuhl Report, Volume 10, Issue 2 Dagstuhl Report
Gemeinsame Dokumente
Programm des Dagstuhl-Seminars [pdf]


This report contains the program and outcomes of Dagstuhl Seminar 20071 on "Foundations of Composite Event Recognition" held at Schloss Dagstuhl, Leibniz Center for Informatics, during February 9-14, 2020.

Composite Event Recognition (CER for short) refers to the activity of detecting patterns in streams of continuously arriving "event" data over, possibly 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 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, and epidemic spread. In each application, CER allows to make sense of streaming data, react accordingly, and prepare for counter-measures.

CER systems become increasingly important as we move from an information economy to an "intelligent economy", where it is not only the accessibility to information that matters but also the ability to analyse, reason, and act upon information, creating competitive advantage in commercial transactions, enabling sustainable management of communities, and promoting appropriate distribution of social, healthcare, and educational services. Current businesses tend to be unable to make sense of the amounts of data that are generated by the increasing number of distributed data sources that are becoming available daily, and rely more and more on CER. As an example, traffic management in smart cities requires the analysis of data from an increasing number of sensors, both mobile (mounted on public transport vehicles and private cars) and stationary (installed on intersections). Using such data streams, CER may be used to detect or even forecast traffic congestions, thus allowing for proactively changing traffic light policies and speed limits, with the aim of reducing carbon emissions, optimising public transportation, and improving the quality of life and productivity of commuters. As another example, in smart energy grids, streaming information from power grid elements sensors, end-user devices, and diverse other sources such as weather forecasts and event schedules can be combined through CER to improve the grid efficiency and meet the rapidly increasing electricity demand.

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 generalise. 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 activities 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 the dynamic setting, in Logic research.

The seminar brought together 39 researchers and practitioners working in domains that are strictly related to CER. The first days of the seminar mainly focused on tutorials and talks that gave an overview of the approaches, techniques, methodologies, and vocabularies used in different communities to refer to CER problems. In particular, the following tutorials were presented:

  • Applications and requirements for CER
  • CER in data management
  • CER in distributed event-based systems
  • Stream reasoning
  • CER in logic and AI
  • CER in business process management

The seminar continued by alternating sessions with focused research talks and group discussions on the following topics, that the participants identified as the most relevant for future investigations and research efforts:

  • CER language formalisms
  • Towards a common framework for CER expressiveness and complexity
  • Evaluation strategies: parallel and distributed processing
  • Uncertainty in CER
  • Pattern induction and composite event forecasting
  • Benchmarking

The final sessions of the seminar focused on reporting the results of the group discussions and in planning follow-up activities, including co-organized workshops and events, joint publications, and projects.

Summary text license
  Creative Commons BY 3.0 Unported license
  Alessandro Margara, Alexander Artikis, Thomas Eiter, and Stijn Vansummeren


  • Artificial Intelligence / Robotics
  • Data Bases / Information Retrieval
  • Semantics / Formal Methods


  • Complex Event Processing
  • Pattern Matching
  • Stream Reasoning
  • Temporal Reasoning
  • Event Algebra


In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.


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Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

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