arXiv AI

Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library

The paper introduces SPICE, a Python framework that reimplements three popular deep‑learning methods for Predictive Process Mining (PPM) using PyTorch. It provides a common, highly configurable base to enable reproducible and robust comparison of PPM models, addressing issues of reproducibility, transparency, and usability. The authors benchmark SPICE against the original reported metrics and fair metrics across 11 datasets.

arXiv Machine Learning

Jun 16

David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

arXiv:2606. 15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering.

By Hans Weytjens, Ingo Weber

llms benchmarks

arXiv AI

Jul 21

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring

arXiv:2607. 17783v1 Announce Type: cross Abstract: Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases.

By Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu, Johannes De Smedt

computer-vision

arXiv AI

Jun 15

DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining

arXiv:2606. 14283v1 Announce Type: cross Abstract: Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring.

By Johannes De Smedt, Jari Peeperkorn, Artem Polyvyanyy, Jochen De Weerdt

llms benchmarks

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

Hugging Face Trending Papers

Jul 20

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring

Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption.

computer-vision

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 Machine Learning

Jun 19

HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data

arXiv:2507. 22524v3 Announce Type: replace Abstract: We propose HGCN(O), a self-tuning toolkit using Graph Convolutional Network (GCN) models for event sequence prediction.

By Fang Wang, Paolo Ceravolo, Ernesto Damiani

arXiv Machine Learning

Jul 27

Scalable Gaussian process inference via neural feature maps

arXiv:2605. 10285v2 Announce Type: replace-cross Abstract: We present a theoretically grounded Gaussian process framework that leverages neural feature maps to construct expressive kernels.

By Anthony Stephenson

benchmarks

arXiv AI

Jun 11

Physics-Distilled Neural Network enabled by Large Language Models for Manufacturing Process-Property Predictive Modeling

arXiv:2606. 11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex 'black-box' models.

By Ge Song, Kiarash Naghavi Khanghah, Anandkumar Patel, Rajiv Malhotra, Hongyi Xu

llms efficiency safety

arXiv AI

Aug 17

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

arXiv AI

Jun 9

Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence

arXiv:2412. 00508v2 Announce Type: replace-cross Abstract: Control structure design is an important but tedious step in P&ID development.

By Lukas Schulze Balhorn, Kevin Degens, Artur M. Schweidtmann

arXiv Machine Learning

Sep 1

Alert: Learning Trigger Functions for Early Classification of Time Series using Deep-RL

arXiv:2502.06584v2 Announce Type: replace Abstract: Early Classification of Time Series (ECTS) is vital in fields like industrial monitoring and medical triage, where quick and accurate predictions a...

By Aur\'elien Renault, Alexis Bondu, Antoine Cornu\'ejols, Vincent Lemaire

reinforcement-learning

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