The paper presents a model‑centric evaluation framework for process discovery that uses node embeddings and Procrustes alignment to compare Petri net models across different datasets. By transforming Petri nets into graph representations, the method computes a bounded cosine‑distance metric that unifies fitness and precision assessments and remains robust to noise. Experiments on real and synthetic logs demonstrate that the embedding‑based indicator outperforms traditional conformance metrics and is sensitive to model degradation while providing activity‑level deviation localization.
arXiv:2610.17702v1 Announce Type: new Abstract: Evaluating the quality of process models discovered from event logs is a fundamental challenge in process mining. Traditional evaluation relies on conf...
The paper introduces two novel techniques for enhancing graph neural networks on Petri nets: a positional encoding derived from directed place‑transition walk return probabilities (PNRWPE) and a diffusion‑kernel‑based message gating using the incidence matrix (DA‑PNGNN). These methods explicitly capture directed cycles and invariants that were previously hidden, leading to significant performance gains. On the Process Discovery Contest 2023 dataset, PNRWPE raises macro balanced accuracy from 0.751 to 0.796, nearly doubling loop detection accuracy, while DA‑PNGNN further improves it to 0.830 and boosts net‑to‑tree retrieval on ProcRosetta by 4.1–5.9 points.
arXiv:2607. 04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining.
The paper introduces a new approach to process model forecasting that uses causal nets instead of traditional directly-follows graphs, enabling explicit representation of concurrency. It forecasts time series of relation and binding counts, reconstructs future process models with AND/XOR semantics, and evaluates them using a protocol that handles partial traces for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance close to re‑mined models and outperform static discovery baselines, though filtering infrequent bindings improves metrics at the cost of losing concurrent behavior.
arXiv:2606. 14283v1 Announce Type: cross Abstract: Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring.
arXiv:2608. 09398v1 Announce Type: cross Abstract: Process discovery is one of the central challenges in process mining.
arXiv:2609.08622v1 Announce Type: cross Abstract: Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represent...
arXiv:2512. 03787v2 Announce Type: replace Abstract: Clinical pathways are specialized healthcare plans that model patient treatment procedures.
arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.
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.
arXiv:2609.14534v1 Announce Type: new Abstract: Predictive Process Monitoring (PPM) aims at predicting at runtime and as early as possible the future states of a process execution. Common tasks inclu...
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...