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:2606. 00041v1 Announce Type: cross Abstract: This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset.
By Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile, Samuele Latorre
arXiv:2607. 04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining.
By Viki Peeva, Wil M. P. van der Aalst
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.
By Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt
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...
By Yiyuan Yang, Zheshun Wu, Yong Chu, Zhenghua Chen, Zenglin Xu, Qingsong Wen
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...
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.
By Maximilian Seeth, Gabriel Marques Tavares, Daniel Schuster
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.
By Anti Alman, Izack Cohen, Avigdor Gal, Fabrizio Maria Maggi, Marco Montali
arXiv:2605. 02488v2 Announce Type: replace Abstract: Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events.
By Periklis Mantenoglou
The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.
By Hugo Math
arXiv:2608. 09398v1 Announce Type: cross Abstract: Process discovery is one of the central challenges in process mining.
By Leah Tacke genannt Unterberg, Lisa L. Mannel, Wil M. P. van der Aalst
arXiv:2510. 04033v2 Announce Type: replace Abstract: Modern computer systems rely on syslog, a universal protocol that records critical events across heterogeneous infrastructure.
By Ayush Noori, Aaron E. Boussina, Hai Ho Bich, James Anibal, Julia Maslinski, Manuel Burger, Martin Faltys, Adam Rodman, Alan Karthikesalingam, Alessandro Blasimme, Annelia Itwaru, Ben Kaplan, Bilal A. Mateen, Christopher A. Longhurst, Daniel Yang, Dave deBronkart, Effy Vayena, Fedor Sergeev, Gauden Galea, Ha Thi Hai Duong, Harold F. Wolf III, Jacob Waxman, Joerg C. Schefold, Joshua C. Mandel, Juliana Rotich, Kenneth D. Mandl, Lily Poursoltan, Maryam Mustafa, Melissa Miles, Nigam H. Shah, Noa Dagan, Pavan Bodanki, Peter Lee, Philipp Koralus, Prathamesh Parchure, Prem Timsina, Ran D. Balicer, Robert Korom, Scott Mahoney, Seth Hain, Tien Yin Wong, Trevor Mundel, Vivek Natarajan, Ankit Sakhuja, Benjamin Glicksberg, C. Louise Thwaites, Gunnar R\"atsch, Karandeep Singh, David A. Clifton, Isaac S. Kohane, Marinka Zitnik