arXiv AI
From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining
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Automated Event Log Generation from Unstructured Text Using Finetuned LLMs
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.
Hugging Face Trending Papers
Sep 1
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 availabilit...
arXiv AI
Jul 31
On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery
arXiv:2604. 22455v2 Announce Type: replace Abstract: A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defines its maximal behavioral boundaries.
arXiv AI
Jun 15
Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches
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.
arXiv AI
Aug 20
LEDGER: Claim-to-Evidence Trace Graphs for Auditing LLM Agents
LEDGER is a tracing and review system for large language model agents that constructs layered trace graphs from observed sessions. It groups raw trace records into Evidence Nodes and Workflow Nodes, anchors artifacts as evidence, and adds typed semantic edges linking claims to supporting actions, artifacts, and checks. The resulting traces reveal workflow decisions, artifact lineage, repair steps, validation coverage, and claim‑support paths for evidence‑centered audit.
arXiv Machine Learning
Jul 7
Framework for Grouping Local Process Models
arXiv:2607. 04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining.