Abstract
Organizations manage numerous business processes to deliver their products and services to customers effectively while adhering to laws, industry standards, or guidelines, summarized here as regulations. Compliance checking involves verifying whether these processes align with the relevant regulations. There has been an increase in the availability of process execution data within IT systems that support these business processes. One valuable operation of process mining – conformance checking – enables a comparison between the actual execution behaviour, represented as event logs, and the desired behaviour outlined in a process model. As a result, conformance checking can be employed for run-time compliance checking, a concept that has been explored and enhanced through various research studies. This work presents a systematic literature review on regulatory compliance checking with conformance checking that offers insights into different domains, the operationalization of regulations, applied conformance checking techniques, and visualizations. Our analysis reveals that several steps are still performed manually, but we anticipate that future advancements in automation can significantly support the goals of run-time compliance checking.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 465904964.
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Notes
- 1.
See https://doi.org/10.6084/m9.figshare.25118849 [Accessed: 03/04/2024].
- 2.
The remaining works did not mention whether the prescriptive model was evaluated or not. Hence, we only capture whether an explicit evaluation took place.
- 3.
Similar to the model validation, the remaining works made no explicit reference to such a requirement; therefore we only differentiate between explicit needs for expert knowledge and no reference to such a need.
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Klessascheck, F., Knoche, T., Pufahl, L. (2024). Reviewing Conformance Checking Uses for Run-Time Regulatory Compliance. In: van der Aa, H., Bork, D., Schmidt, R., Sturm, A. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2024 2024. Lecture Notes in Business Information Processing, vol 511. Springer, Cham. https://doi.org/10.1007/978-3-031-61007-3_9
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