Integrating ISA and Part-of Domain Knowledge into Process Model Discovery

Alessio Bottrighi, Marco Guazzone, Giorgio Leonardi (), Stefania Montani, Manuel Striani and Paolo Terenziani
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Alessio Bottrighi: Department of Science, Technology and Innovation, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
Marco Guazzone: Department of Science, Technology and Innovation, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
Giorgio Leonardi: Department of Science, Technology and Innovation, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
Stefania Montani: Department of Science, Technology and Innovation, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
Manuel Striani: Department of Science, Technology and Innovation, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy
Paolo Terenziani: Department of Science, Technology and Innovation, Università del Piemonte Orientale, Viale Teresa Michel 11, 15121 Alessandria, Italy

Future Internet, 2022, vol. 14, issue 12, 1-29

Abstract: The traces of process executions are a strategic source of information, from which a model of the process can be mined. In our recent work, we have proposed SIM (semantic interactive miner), an innovative process mining tool to discover the process model incrementally: it supports the interaction with domain experts, who can selectively merge parts of the model to achieve compactness, generalization, and reduced redundancy. We now propose a substantial extension of SIM, making it able to exploit (both automatically and interactively) pre-encoded taxonomic knowledge about the refinement (ISA relations) and composition (part-of relations) of process activities, as is available in many domains. The extended approach allows analysts to move from a process description where activities are reported at the ground level to more user-interpretable/compact descriptions, in which sets of such activities are abstracted into the “macro-activities” subsuming them or constituted by them. An experimental evaluation based on a real-world setting (stroke management) illustrates the advantages of our approach.

Keywords: knowledge-based process model discovery; process model abstraction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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