2026-07-22T00:13:46Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:19098 2024-03-06T11:03:11Z ddc:004 open_access

Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks Bregoli, Alessandro Rathsman, Karin Scutari, Marco Stella, Fabio Mogensen, Søren Wengel event model continuous-time Bayesian network alarm network graphical models event cascade 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Alessandro Bregoli and Karin Rathsman and Marco Scutari and Fabio Stella and Søren Wengel Mogensen 2023 Is Part Of LIPIcs, Volume 278, 30th International Symposium on Temporal Representation and Reasoning (TIME 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.TIME.2023.8 urn:nbn:de:0030-drops-190980 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2023.8 eng https://creativecommons.org/licenses/by/4.0/legalcode