arXiv Machine Learning

Framework for Grouping Local Process Models

arXiv:2607. 04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining.

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.

By Stefano Branchi, Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini, Riccardo Graziosi, Francesca Meneghello, Massimiliano Ronzani

reinforcement-learning benchmarks

arXiv AI

Aug 11

Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework

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 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.

By Anti Alman, Izack Cohen, Avigdor Gal, Fabrizio Maria Maggi, Marco Montali

agents

arXiv Machine Learning

Jun 15

Adaptive Identification and Modeling of Clinical Pathways with Process Mining

arXiv:2512. 03787v2 Announce Type: replace Abstract: Clinical pathways are specialized healthcare plans that model patient treatment procedures.

By Francesco Vitale, Nicola Mazzocca

rag benchmarks

arXiv Machine Learning

Jun 16

All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams

arXiv:2604. 22476v2 Announce Type: replace-cross Abstract: Disciplines such as business process management and process mining aid organizations by discovering insights about processes on the basis of recorded event data.

By Marco Pegoraro, Jonas Seng, Dustin Heller, Wil M. P. van der Aalst, Kristian Kersting

rag computer-vision

arXiv Machine Learning

Jul 31

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.

By Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn

llms benchmarks

arXiv AI

Jun 2

Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways

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 Machine Learning

Jun 11

Few-Shot Resampling for Scalable Statistically-Sound Data Mining

arXiv:2606. 11235v1 Announce Type: new Abstract: A key step in knowledge discovery is the evaluation of data mining results.

By Leonardo Pellegrina, Fabio Vandin

arXiv AI

Jun 15

Abstracting Cross-Domain Action Sequences into Interpretable Workflows

arXiv:2606. 14654v1 Announce Type: new Abstract: Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work.

By Gaurav Verma, Scott Counts

llms safety

arXiv Machine Learning

Jun 16

Agent trajectories as programs: fingerprinting and programming coding-agent behavior

arXiv:2606. 16988v1 Announce Type: cross Abstract: Benchmark scores tell you what an agent got right; they do not tell you how it got there.

By Hamidah Oderinwale

agents benchmarks

arXiv AI

Jun 18

Improve Large Language Model Systems with User Logs

arXiv:2602. 06470v3 Announce Type: replace-cross Abstract: Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality data and diminishing returns from rising computational costs.

By Changyue Wang, Weihang Su, Qingyao Ai, Xingzhao Yue, Rui Zhang, Xiaojia Chang, Yiqun Liu

llms rag benchmarks

arXiv AI

2d ago

Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes

arXiv:2608. 14367v1 Announce Type: cross Abstract: The early detection of delayed cases in business processes is a critical capability for organizations.

By Keyvan Amiri Elyasi, Lukas Kirchdorfer, Heiner Stuckenschmidt

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