Towards Multi-perspective Conformance Checking with Fuzzy Sets.

Sicui Zhang; Laura Genga; Hui Yan; Hongchao Nie; Xudong Lu; Uzay Kaymak

doi:10.9781/ijimai.2021.02.013

Authors

Sicui Zhang Zhejiang University
Laura Genga Eindhoven University of Technology
Hui Yan Hainan University
Hongchao Nie Philips Research.
Xudong Lu Zhejiang University
Uzay Kaymak Zhejiang University

DOI:

https://doi.org/10.9781/ijimai.2021.02.013

Keywords:

Business Processes, Conformance Checking, Data Perspective, Fuzzy Logic

Supporting Agencies

The research leading to these results has received funding from the Brain Bridge Project sponsored by Philips Research.

Abstract

Nowadays organizations often need to employ data-driven techniques to audit their business processes and ensure they comply with laws and internal/external regulations. Failing in complying with the expected process behavior can indeed pave the way to inefficiencies or, worse, to frauds or abuses. An increasingly popular approach to automatically assess the compliance of the executions of organization processes is represented by alignment-based conformance checking. These techniques are able to compare real process executions with models representing the expected behaviors, providing diagnostics able to pinpoint possible discrepancies. However, the diagnostics generated by state of the art techniques still suffer from some limitations. They perform a crisp evaluation of process compliance, marking process behavior either as compliant or deviant, without taking into account the severity of the identified deviation. This hampers the accuracy of the obtained diagnostics and can lead to misleading results, especially in contexts where there is some tolerance with respect to violations of the process guidelines. In the present work, we discuss the impact and the drawbacks of a crisp deviation assessment approach. Then, we propose a novel conformance checking approach aimed at representing actors’ tolerance with respect to process deviations, taking it into account when assessing the severity of the deviations. As a proof of concept, we performed a set of synthetic experiments to assess the approach. The obtained results point out the potential of the usage of a more flexible evaluation of process deviations, and its impact on the quality and the interpretation of the obtained diagnostics.

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