| Autor: |
Maneschijn, Dennis G.J.C., Bemthuis, Rob H., Bukhsh, Faiza A., Iacob, Maria-Eugenia, Filipe, Joaquim, Smialek, Michal, Brodsky, Alexander, Hammoudi, Slimane |
| Přispěvatelé: |
Pervasive Systems, Digital Society Institute, Datamanagement & Biometrics, Industrial Engineering & Business Information Systems |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Proceedings of the 24th International Conference on Enterprise Information Systems, 1, 137-147 |
| Popis: |
Process mining derives knowledge of the execution of processes through analyzing behavior as observed from real-life events. While benefits of process mining are widely acknowledged, finding an adequate level of detail at which a mined process model is suitable for a specific stakeholder is still an ongoing challenge. Process models can be mined at different levels of abstraction, often resulting in either highly complex or highly abstract process models. This may have an important impact on the comprehensibility of the process model, which can also differ from the perspective of a particular stakeholder. To address this problem from a stakeholder-centric perspective, we propose a methodology for determining an appropriate level of process model abstraction. To this end, we use quantitative metrics on process models as well as a qualitative evaluation by using a technology acceptance model (TAM). A logistics case study involving the fuzzy process mining discovery algorithm shows init ial evidence that the use of appropriate abstraction levels is key when considering the needs of various stakeholders. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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