A generic process mining framework for uncovering hierarchical process model

Tang, Junya, Li, Li, Yu, Qingyun, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Zammit, Joseph Paul and Francalanza, Emmanuel 2025. A generic process mining framework for uncovering hierarchical process model. Presented at: 31st International Conference on Engineering, Technology, and Innovation- ICE IEEE/ITMC, Valencia, Spain, 16-19 June 2025. Proceedings of the International Conference on Engineering, Technology, and Innovation. IEEE, pp. 1-10. 10.1109/ice/itmc65658.2025.11106526

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Abstract

Taking advantage of the strengths of knowledge engineering and data science, process mining has recently become a popular approach to process management research. Process mining research has focused on creating process models, checking conformance and analysing bottlenecks. Previous studies have helped organisers understand and improve processes in some fields, and some specific questions (for example, discovering a finished process model from structured data) have been studied. However, for a more general study, it is essential to correlate process mining under different conditions and form a generic process mining framework. This paper proposed a generic process mining framework for uncovering hierarchical process models under different conditions. Firstly, the proposed framework unifies process model discovery approaches for structured and unstructured data, providing a general solution that can perform those. Secondly, the framework proposed an incremental solution for ongoing processes based on the approaches for completed processes. Finally, taking unstructured data as a case, a knowledge extraction-based process discovery approach is proposed to build a hierarchical process model by document clustering and sub-process modelling. Experimental studies using real-world data collected from a design project revealed the merits of the proposed approach. The proposed approach can discover more understandable, adaptive process models.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	IEEE
ISBN:	9798331585358
ISSN:	2334-315X
Last Modified:	18 Aug 2025 13:19
URI:	https://orca.cardiff.ac.uk/id/eprint/180501

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