Towards Multi-perspective Conformance Checking with Aggregation Operations

Sicui Zhang; Laura Genga; Lukas Dekker; Hongchao Nie; Xudong Lu; Huilong Duan; Uzay Kaymak

doi:10.1007/978-3-030-50146-4_17

Towards Multi-perspective Conformance Checking with Aggregation Operations

Sicui Zhang, Laura Genga, Lukas Dekker, Hongchao Nie, Xudong Lu, Huilong Duan, Uzay Kaymak

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

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
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	https://doi.org/10.1007/978-3-030-50146-4_17
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
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
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

Access to Document

10.1007/978-3-030-50146-4_17

Cite this

Zhang, S., Genga, L., Dekker, L., Nie, H., Lu, X., Duan, H., & Kaymak, U. (2020). Towards Multi-perspective Conformance Checking with Aggregation Operations. In M.-J. Lesot, S. Vieira, M. Z. Reformat, J. P. Carvalho, A. Wilbik, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings (pp. 215-229). (Communications in Computer and Information Science; Vol. 1237 CCIS). Springer. https://doi.org/10.1007/978-3-030-50146-4_17

Zhang, Sicui ; Genga, Laura ; Dekker, Lukas et al. / Towards Multi-perspective Conformance Checking with Aggregation Operations. Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. editor / Marie-Jeanne Lesot ; Susana Vieira ; Marek Z. Reformat ; João Paulo Carvalho ; Anna Wilbik ; Bernadette Bouchon-Meunier ; Ronald R. Yager. Springer, 2020. pp. 215-229 (Communications in Computer and Information Science).

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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{\textquoteright}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.",

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Zhang, S, Genga, L , Dekker, L, Nie, H, Lu, X, Duan, H & Kaymak, U 2020, Towards Multi-perspective Conformance Checking with Aggregation Operations. in M-J Lesot, S Vieira, MZ Reformat, JP Carvalho, A Wilbik, B Bouchon-Meunier & RR Yager (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Communications in Computer and Information Science, vol. 1237 CCIS, Springer, pp. 215-229, 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, Lisbon, Portugal, 15/06/20. https://doi.org/10.1007/978-3-030-50146-4_17

Towards Multi-perspective Conformance Checking with Aggregation Operations. / Zhang, Sicui; Genga, Laura ; Dekker, Lukas et al.
Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. ed. / Marie-Jeanne Lesot; Susana Vieira; Marek Z. Reformat; João Paulo Carvalho; Anna Wilbik; Bernadette Bouchon-Meunier; Ronald R. Yager. Springer, 2020. p. 215-229 (Communications in Computer and Information Science; Vol. 1237 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Duan, Huilong

AU - Kaymak, Uzay

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AB - 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.

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Zhang S, Genga L , Dekker L, Nie H, Lu X, Duan H et al. Towards Multi-perspective Conformance Checking with Aggregation Operations. In Lesot MJ, Vieira S, Reformat MZ, Carvalho JP, Wilbik A, Bouchon-Meunier B, Yager RR, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Springer. 2020. p. 215-229. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-50146-4_17

Towards Multi-perspective Conformance Checking with Aggregation Operations

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