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Enhancing Process Discovery by Optimizing Imprecise Sub-processes
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Orientador(es)
Resumo(s)
Process discovery aims to derive a process model that accurately represents the observed behavior in an event log. As a state-of-the-art process discovery technique, Inductive Miner (IM) generates sound process models (i.e., free of deadlocks) while ensuring optimal replay fitness. However, IM may sometimes produce over-generalized process models with locally imprecise structures, often resulting in the creation of so-called flower structures. To address this limitation, this paper presents a novel technique that refines the process model generated by IM by optimizing its imprecise sub-processes. Specifically, the technique begins by identifying and extracting sub-logs corresponding to imprecise sub-processes in the initial IM-generated process model. Then, these imprecise sub-processes are iteratively optimized using a frequency-based filtering mechanism applied to the sub-logs. Once optimized, the imprecise sub-processes in the initial process model are replaced by the optimized ones, generating a set of candidates process models. Finally, the candidate with the best quality, in terms of fitness and precision, is selected as the final optimized process models. The proposed technique has been implemented as a plugin for the open-source process mining platform ProM. Through comparisons with state-of-the-art process discovery techniques using 10 publicly available real-life event logs, the experimental results demonstrate that the proposed method achieves an average absolute improvement of 0.173 in F-measure over its IMi variant, while also exhibiting competitive performance relative to other state-of-the-art approaches.
Descrição
Yan, J., Liu, C., Zeng, Q., Cao, J., Wu, Y., Ouyang, C., & Cheng, L. (2026). Enhancing Process Discovery by Optimizing Imprecise Sub-processes. IEEE Transactions on Services Computing, 19(1), 337-350. https://doi.org/10.1109/TSC.2026.3652280 --- This work was partly supported by the National Natural Science Foundation of China under Grant 62472264 and 52574256, the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13, the Natural Science Key Basic Research Project of Shandong Province under Grant ZR2025ZD17, the Taishan Scholar Program of Shandong Province under Grant TSTP20250506, and the national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UID/04152/2025 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS and UID/PRR/04152/2025.
Palavras-chave
Process Mining Process Discovery Inductive Miner Sub-process Optimization Petri Nets Hardware and Architecture Computer Science Applications Computer Networks and Communications Information Systems and Management