Scientific

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 …

A Novel Way to Evaluate Medical Discharge Predictions: A Research Paper

Posted onAugust 27, 2025August 27, 2025AuthorEric VerbeekLeave a comment

van der Haas, Y., Medeiros de Carvalho, R., van Dijk, T., van Dongen, B. F., & Plas, R. (2025). A Novel Way to Evaluate Medical Discharge Predictions: A Research Paper. Paper presented at2st International Workshop on Process Mining Applications for Healthcare, PM4H25, Pavia, Italië.

WISE: Unraveling Business Process Metrics with Domain Knowledge

Posted onJuly 24, 2025July 24, 2025AuthorEric VerbeekLeave a comment

Jessen, U., & Fahland, D. (2024). WISE: Unraveling Business Process Metrics with Domain Knowledge. https://doi.org/10.48550/arXiv.2410.04387

Challenges of Anomaly Detection in the Object-Centric Setting: Dimensions and the Role of Domain Knowledge

Posted onJuly 24, 2025July 24, 2025AuthorEric VerbeekLeave a comment

Berti, A., Jessen, U., van der Aalst, W. M. P., & Fahland, D. (2024). Challenges of Anomaly Detection in the Object-Centric Setting: Dimensions and the Role of Domain Knowledge. arXiv.org. https://doi.org/10.48550/arXiv.2407.09023 Abstract Object-centric event logs, allowing events related to different objects of different object types, represent naturally the execution of business processes, such as ERP Read More …

Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) – Core Model, Design Space, and Lessons Learned

Posted onApril 9, 2025AuthorEric VerbeekLeave a comment

Fahland, D., Montali, M., Lebherz, J., van der Aalst, W. M. P., van Asseldonk, M., Blank, P., Bosmans, L., Brenscheidt, M., Di Ciccio, C., Delgado, A., Calegari, D., Peeperkorn, J., Verbeek, E., Vugs, L., & Wynn, M. T. (2024). Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) – Core Model, Design Space, Read More …

Exact and Approximated Log Alignments for Processes with Inter-case Dependencies

Posted onNovember 15, 2024AuthorEric VerbeekLeave a comment

Sommers, D., Sidorova, N., & van Dongen, B. F. (2023). Exact and Approximated Log Alignments for Processes with Inter-case Dependencies. arXiv, 2023, Article 2304.05210. https://doi.org/10.48550/arXiv.2304.05210 Abstract The execution of different cases of a process is often restricted by inter-case dependencies through e.g., queueing or shared resources. Various high-level Petri net formalisms have been proposed that Read More …

How well can large language models explain business processes?

Posted onMay 29, 2024AuthorEric VerbeekLeave a comment

Fahland, D., Fournier, F., Limonad, L., Skarbovsky, I., & Swevels, A. J. E. (2024). How well can large language models explain business processes? arXiv, abs/2401.12846. https://doi.org/10.48550/arXiv.2401.12846 Abstract Large Language Models (LLMs) are likely to play a prominent role in future AI-augmented business process management systems (ABPMSs) catering functionalities across all system lifecycle stages. One such Read More …

Chit-Chat or Deep Talk: Prompt Engineering for Process Mining

Posted onSeptember 26, 2023September 26, 2023AuthorEric VerbeekLeave a comment

Jessen, U., Sroka, M., & Fahland, D. (2023). Chit-Chat or Deep Talk: Prompt Engineering for Process Mining. CoRR, abs/2307.09909. https://doi.org/10.48550/arXiv.2307.09909

Clustering-based Aggregations for Prediction in Event Streams

Posted onJune 23, 2023June 23, 2023AuthorEric VerbeekLeave a comment

Spenrath, Y., Hassani, M., & Dongen, B. F. V. (2022). Clustering-based Aggregations for Prediction in Event Streams. CoRR, abs/2210.09738. https://doi.org/10.48550/arXiv.2210.09738

Extracting and Pre-Processing Event Logs

Posted onJanuary 27, 2023January 27, 2023AuthorEric VerbeekLeave a comment

Fahland, D. (2022). Extracting and Pre-Processing Event Logs. CoRR, abs/2211.04338. https://doi.org/10.48550/arXiv.2211.04338

Uncovering Complex Relations in Patient Pathways based on Statistics: the Impact of Clinical Actions

Posted onMay 31, 2022May 31, 2022AuthorEric VerbeekLeave a comment

Koorn, J. J., Lu, X., Mannhardt, F., Leopold, H., & Reijers, H. A. (2022). Uncovering Complex Relations in Patient Pathways based on Statistics: the Impact of Clinical Actions. https://doi.org/10.24251/HICSS.2022.503 Abstract Process mining is a family of techniques that can aid healthcare organizations in improving their processes. Most existing process mining techniques do not provide insights Read More …

A Framework for Efficient Memory Utilization in Online Conformance Checking

Posted onApril 1, 2022November 1, 2024AuthorEric VerbeekLeave a comment

Zaman, R., Hassani, M., & van Dongen, B. F. (2021). A Framework for Efficient Memory Utilization in Online Conformance Checking. arXiv.org. https://arxiv.org/pdf/2112.13640.pdf Abstract Conformance checking (CC) techniques of the process mining field gauge the conformance of the sequence of events in a case with respect to a business process model, which simply put is an Read More …

What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning

Posted onApril 1, 2022April 1, 2022AuthorEric VerbeekLeave a comment

Ketykó, I., Mannhardt, F., Hassani, M., & van Dongen, B. F. (2021). What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning. CoRR, abs/2110.10225. https://arxiv.org/abs/2110.10225 Abstract Deep Learning is proven to be an effective tool for modeling sequential data as shown by the success in Natural Language, Computer Vision and Signal Read More …

Augmented Business Process Management Systems: A Research Manifesto

Posted onFebruary 28, 2022February 28, 2022AuthorEric VerbeekLeave a comment

Dumas, M., Fournier, F., Limonad, L., Marrella, A., Montali, M., Rehse, J-R., Accorsi, R., Calvanese, D., Giacomo, G. D., Fahland, D., Gal, A., Rosa, M. L., Völzer, H., & Weber, I. (2022). Augmented Business Process Management Systems: A Research Manifesto. CoRR, abs/2201.12855. https://dblp.org/db/journals/corr/corr2201.html#abs-2201-12855

BPIC’2018: Mining Concept Drift in Performance Spectra of Processes

Posted onFebruary 25, 2021AuthorEric VerbeekLeave a comment

Denisov, V. V., Belkina, E., & Fahland, D. (2018). BPIC’2018: Mining Concept Drift in Performance Spectra of Processes. (BPI Challenge 2018). https://doi.org/10.4121/uuid:3301445f-95e8-4ff0-981f1f204972

Using graph data structures for event logs

Posted onFebruary 25, 2021February 25, 2021AuthorEric VerbeekLeave a comment

Esser, S., & Fahland, D. (2019). Using graph data structures for event logs. https://doi.org/10.5281/zenodo.3333831 Abstract Process mining as described in by Wil van der Aalst in is a combination of data mining and business process management to a new discipline. The general purpose of process mining is to derive process insights from event data captured Read More …

Log skeletons: a classification approach to process discovery

Posted onFebruary 19, 2021AuthorEric VerbeekLeave a comment

Verbeek, H. M. W., & Medeiros de Carvalho, R. (2018). Log skeletons: a classification approach to process discovery. arXiv.org. http://arxiv.org/abs/1806.08247 Abstract To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify Read More …

Framework for process discovery from sensor data

Posted onJanuary 18, 2021AuthorEric VerbeekLeave a comment

Koschmider, A., Janssen, D., & Mannhardt, F. (2020). Framework for process discovery from sensor data. CEUR Workshop Proceedings, 2628, 32-38. Abstract Process mining can give valuable insights into how real-life activities are performed when extracting meaningful activities instances from raw sensor events. However, in many cases, the event data generated during the execution of a Read More …