A Process Tree-Based Incomplete Event Log Repair Approach

The low quality of business process event logs—particularly the widespread occurrence of incomplete traces—poses significant challenges to the reliability, accuracy, and efficiency of process mining analysis. In real-world scenarios, these data imperfections severely undermine the practical value of process mining techniques. The primary research problem addressed in this study is the inefficiency and limited effectiveness of existing Petri-net-based incomplete trace repair approaches, which often struggle to accurately recover missing events in the presence of complex and nested loop structures. To tackle these limitations, we aim to develop a faster and more accurate approach for repairing incomplete event logs. Specifically, we propose a novel repair approach based on process trees as an alternative to traditional Petri nets, thus alleviating issues such as state space explosion. Our approach incorporates process tree model decomposition and innovative branch indexing techniques, enabling rapid localization of candidate branches for repair and a significant reduction in the solution space. Furthermore, by leveraging activity information within the traces, our approach achieves efficient and precise repair of loop nodes through a single traversal of the process tree. To comprehensively evaluate our approach, we conduct experiments on four real-life and five synthetic event logs, comparing performance against state-of-the-art techniques. The experimental results demonstrate that our approach consistently delivers repair accuracies exceeding 70%, with time efficiency improved by up to three orders of magnitude. These findings validate the superior accuracy, efficiency, and scalability of the proposed approach, highlighting its strong potential for practical applications in business process mining.