Working paper – Process Analytics

Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction

Posted onJuly 21, 2026July 21, 2026AuthorEric VerbeekLeave a comment

Hassani, M., Verbeek, T., & van Straten, S. (2026). Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction. arXiv.org. https://doi.org/10.48550/arXiv.2604.00653 Abstract Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume Read More …

A Ground Truth Approach for Assessing Process Mining Techniques

Posted onJanuary 26, 2026AuthorEric VerbeekLeave a comment

Sommers, D., Sidorova, N., & van Dongen, B. F. (2025). A Ground Truth Approach for Assessing Process Mining Techniques. arXiv.org. https://doi.org/10.48550/arXiv.2501.14345 Abstract The assessment of process mining techniques using real-life data is often compromised by the lack of ground truth knowledge, the presence of non-essential outliers in system behavior and recording errors in event logs. Read More …

Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring

Posted onOctober 16, 2025October 16, 2025AuthorEric VerbeekLeave a comment

van Straten, S., Padella, A., & Hassani, M. (2025). Leveraging Data Augmentation and Siamese Learning for Predictive Process Monitoring. arXiv.org. https://doi.org/10.48550/arXiv.2507.18293 Abstract Predictive Process Monitoring (PPM) enables forecasting future events or outcomes of ongoing business process instances based on event logs. However, deep learning PPM approaches are often limited by the low variability and small Read More …