Abstract
Conformance checking techniques are widely adopted to validate process executions against a set of constraints describing the expected behavior. However, most approaches adopt a crisp evaluation of deviations, with the result that small violations are considered at the same level of significant ones. Furthermore, in the presence of multiple data constraints the overall deviation severity is assessed by summing up each single deviation. This approach easily leads to misleading diagnostics; furthermore, it does not take into account user’s needs, that are likely to differ depending on the context of the analysis. We propose a novel methodology based on the use of aggregation functions, to assess the level of deviation severity for a set of constraints, and to customize the tolerance to deviations of multiple constraints.
Original language | English |
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Title of host publication | Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings |
Editors | Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager |
Publisher | Springer |
Pages | 215-229 |
Number of pages | 15 |
ISBN (Print) | 9783030501457 |
DOIs | |
Publication status | Published - 1 Jan 2020 |
Event | 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 - Lisbon, Portugal Duration: 15 Jun 2020 → 19 Jun 2020 |
Publication series
Name | Communications in Computer and Information Science |
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Volume | 1237 CCIS |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 |
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Country/Territory | Portugal |
City | Lisbon |
Period | 15/06/20 → 19/06/20 |
Funding
Acknowledgements. The research leading to these results has received funding from the Brain Bridge Project sponsored by Philips Research.
Keywords
- Conformance checking
- Data perspective
- Fuzzy aggregation