Creative Commons Attribution 4.0 license (CC BY 4.0)when quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TIME.2023.8
URN: urn:nbn:de:0030-drops-190980
URL: https://drops.dagstuhl.de/opus/volltexte/2023/19098/
Bregoli, Alessandro ; Rathsman, Karin ; Scutari, Marco ; Stella, Fabio ; Mogensen, Søren Wengel
Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks
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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.
BibTeX - Entry
@InProceedings{bregoli_et_al:LIPIcs.TIME.2023.8,
author = {Bregoli, Alessandro and Rathsman, Karin and Scutari, Marco and Stella, Fabio and Mogensen, S{\o}ren Wengel},
title = {{Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks}},
booktitle = {30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
pages = {8:1--8:21},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-298-3},
ISSN = {1868-8969},
year = {2023},
volume = {278},
editor = {Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2023/19098},
URN = {urn:nbn:de:0030-drops-190980},
doi = {10.4230/LIPIcs.TIME.2023.8},
annote = {Keywords: event model, continuous-time Bayesian network, alarm network, graphical models, event cascade}
}
| Keywords: | event model, continuous-time Bayesian network, alarm network, graphical models, event cascade | |
| Seminar: | 30th International Symposium on Temporal Representation and Reasoning (TIME 2023) | |
| Issue date: | 2023 | |
| Date of publication: | 18.09.2023 |
