Abstract—Event logs record key activity data for process mining, yet quality defects such as attribute and case errors undermine economic benefits by corrupting critical operational insights. Identical timestamp errors occur when multiple events of a process instance mistakenly share the same timestamp—distort execution sequences, misrepresent concurrency, and compromise analysis accuracy. These errors manifest as multi-layered problems, with case-level sequence errors being particularly impactful. Current solutions inadequately correct consecutive event ordering errors due to incomplete utilization of log sequence information. We propose LLM-based Event Sequence Reconstruction (LESR), a novel framework integrating probabilistic ranking with fine-tuned Large Language Model (LLM) to resolve sequence faults. Its two-phase LLM architecture first generates candidates then selects optimally. Evaluated on 5 real and synthetic logs from e-commerce, healthcare, and public administration domains, LESR significantly outperforms baselines in repairing sequence errors caused by identical timestamps.

Keywords—event log repair, process mining, process data quality, large language model

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Cite: Zhengxu Huang, Yishi Zhao, Jianga Shang, and Shi Ying, "LESR: LLM-Based Event Sequence Reconstruction for Repairing Identical Timestamp Errors in Logs," Journal of Software, vol. 20, no. 2, pp. 95-109, 2025.


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