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Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks

Authors Alessandro Bregoli , Karin Rathsman , Marco Scutari , Fabio Stella , Søren Wengel Mogensen

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Author Details

Alessandro Bregoli
  • Department of Informatics, Systems and Communication, University of Milano-Bicocca, Italy
Karin Rathsman
  • European Spallation Source ERIC, Lund, Sweden
Marco Scutari
  • Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Lugano, Switzerland
Fabio Stella
  • Department of Informatics, Systems and Communication, University of Milano-Bicocca, Italy
Søren Wengel Mogensen
  • Department of Automatic Control, Lund University, Sweden

Funding

  • Mogensen, Søren Wengel: The work of SWM was funded by a DFF-International Postdoctoral Grant (0164-00023B) from Independent Research Fund Denmark. SWM is a member of the ELLIIT Strategic Research Area at Lund University.

Acknowledgements

The authors would like to thank Per Nilsson for sharing his knowledge about the cryogenics plant and for providing valuable feedback on the work presented in this paper.

Cite AsGet BibTex

Alessandro Bregoli, Karin Rathsman, Marco Scutari, Fabio Stella, and Søren Wengel Mogensen. Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks. In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.TIME.2023.8

Abstract

Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is important to understand which states of the system trigger them. For this purpose, we propose a modeling framework based on continuous-time Bayesian networks (CTBNs) to analyze cascading behavior in complex systems. This framework allows us to describe how events propagate through the system and to identify likely sentry states, that is, system states that may lead to imminent cascading behavior. Moreover, CTBNs have a simple graphical representation and provide interpretable outputs, both of which are important when communicating with domain experts. We also develop new methods for knowledge extraction from CTBNs and we apply the proposed methodology to a data set of alarms in a large industrial system.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Markov processes
  • Mathematics of computing → Bayesian networks
Keywords
  • event model
  • continuous-time Bayesian network
  • alarm network
  • graphical models
  • event cascade

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