arXiv:2607. 04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper introduces a scalable framework that uses finetuned large language models (LLMs) to translate unstructured textual resources into structured event logs for process mining. By creating a new text-to-log dataset and finetuning LLMs on it, the authors demonstrate that the resulting models produce high‑fidelity event logs, outperforming few‑shot or zero‑shot prompting methods. This approach enables previously unused organizational data, such as incident tickets and manuals, to be incorporated into process mining workflows.
The paper examines how event logs used in process mining can fail to reveal concurrent versus sequential activities, using a hospital example where blood tests and imaging may occur simultaneously or in alternating order. It demonstrates that standard stochastic language approaches only expose the assumptions of their discovery algorithms, often misrepresenting concurrency. The authors argue that the key to distinguishing concurrent behavior lies in the choice of recorded data—such as precise start and end times or object‑centric ordering—rather than simply increasing sample size.
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 availabilit...
arXiv:2609.07984v1 Announce Type: new Abstract: Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime prediction...
The paper examines how event data from hospital processes can obscure whether activities occur concurrently or sequentially. It shows that standard event‑log approaches, based on stochastic language, often fail to distinguish concurrency because any log can be explained by a model with no concurrent events. The authors argue that the key to resolving this ambiguity lies in the choice of what is recorded—such as precise start and end times or object‑centric ordering—rather than simply collecting more data.
arXiv:2303. 09209v2 Announce Type: replace Abstract: Prescriptive Process Monitoring is an emerging area within Process Mining that focuses on recommending actions to optimize business outcomes.