Journal of Information Science Theory and Practice

Korea Institute of Science and Technology Information (한국과학기술정보연구원 과학기술정보센터)
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Extended Inductive Miner to Discover Semantic Annotated Process Model: A Variability-Based Approach

  • Aicha Khannat (Department of AlQualsadi Research Team ENSIAS, Mohammed V University of Rabat (UM5)) ;
  • Hanae Sbai (Department of Mathematics, Computer Science and Applications Laboratory, Hassan II University of Casablanca (UH2C)) ;
  • Laila Kjiri (Department of AlQualsadi Research Team ENSIAS, Mohammed V University of Rabat (UM5))
  • Received : 2024.03.24
  • Accepted : 2024.06.04
  • Published : 2024.12.30
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Abstract

In recent years, the concept of a configurable process model has made many contributions, with the need for generic and reusable models. The high demand for this type of generic model is accompanied by quality requirements, as these models must be as comprehensive as possible to facilitate the task of customization. Many approaches exist to deal with configurable process models, especially in the field of process mining, including its three types of techniques: discovery, conformance, and enhancement. However, there is a lack of semantic representation in the resulting models. In this study, we propose a novel automated approach based on the extension of the Inductive Miner algorithm used to discover business process models. This method discovers a semantically annotated configurable process model using two ontologies: variability ontology and domain ontology-related concepts, which will improve and facilitate the configurable process model customization.

Keywords

References

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