Automated Event Log Generation from Unstructured Text Using Finetuned LLMs
Process mining (PM) provides a powerful framework for discovering and optimizing operational processes from event data. However, the efficacy of PM techniques is strictly predicated on the availability of structured event logs. Thus far, event logs have often been laboriously created by domain and process mining experts. This costly effort causes large portions of organizational knowledge, including incident tickets, manuals, and textual reports, to remain underutilized. We address this bottleneck by investigating the efficacy of Large Language Models (LLMs) as automated data translators. We propose a scalable framework that leverages LLMs as data translators to bridge the gap between uns...
| Authors | Maximilian Seeth, Gabriel Marques Tavares, Daniel Schuster |
|---|---|
| Primary category | cs.AI |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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