Publications
You can also find my articles on my Google Scholar profile.
Journal publications
Published
- Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt, “Validation Set Sampling for Predictive Process Monitoring”, Information Systems (2024), https://doi.org/10.1016/j.is.2023.102330
- Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt, “Global conformance checking measures using shallow representation and deep learning”, Engineering Applications of Artificial Intelligence (2023), https://doi.org/10.1016/j.engappai.2023.106393
- Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt, “Can recurrent neural networks learn process model structure?”, Journal of Intelligent Information Systems (2022), https://doi.org/10.1007/s10844-022-00765-x
In Submission
- Alexander Stevens, Jari Peeperkorn, Johannes De Smedt \& Jochen De Weerdt, “Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders”
- Yongbo Yu, Jari Peeperkornn, Johannes De Smedt \& Jochen De Weerdt, “A Benchmarking Study on Process Model Forecasting: Univariate vs. Multivariate Approaches”
- Jari Peeperkorn, Johannes De Smedt \& Jochen De Weerdt, “Model-driven Stochastic Trace Clustering”
In conference/workshop proceedings
Published
- Jari Peeperkorn, Simon De Vos, “Achieving Group Fairness through Independence in Predictive Process Monitoring”, Advanced Information Systems Engineering, Lecture Notes in Computer Science, vol 15701, CAiSE 2025
- Thais Rodrigues Neubauer, Jari Peeperkorn, Jochen De Weerdt, Marcelo Fantinato, Sarajane Marques Peres, “Enhancing Remaining Time Prediction in Business Processes through Graph Embedding”, Proceedings of the 58th Hawaii International Conference on System Sciences, HICSS 2025
- Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt “Multivariate Approaches for Process Model Forecasting”, Process Mining Workshops (ML4PM), ICPM 2024
- Thais Rodrigues Neubauer, Jari Peeperkorn, Sarajane Marques Peres, Jochen De Weerdt, Marcelo Fantinato, “Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration”, 2023 International Conference on Machine Learning and Applications (ICMLA), https://doi.org/10.1109/ICMLA58977.2023.00087
- Alexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt, “Manifold Learning for Adversarial Robustness in Predictive Process Monitoring”, 2023 5th International Conference on Process Mining (ICPM), “https://doi.org/10.1109/ICPM60904.2023.10271991”
- Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Jochen De Weerdt, Wil MP van der Aalst, “Discovering high-quality process models despite data scarcity”, : Companion Proceedings of the 42nd International Conference on Conceptual Modeling: ER Forum 2023, https://ceur-ws.org/Vol-3618/forum_paper_23.pdf
- Alexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt, “Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks”, 2022 4th International Conference on Process Mining (ICPM), “https://doi.org/10.1109/ICPM57379.2022.9980753”
- Jari Peeperkorn, Carlos Ortega Vázquez, Alexander Stevens, Johannes De Smedt, Seppe vanden Broucke, Jochen De Weerdt, “Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs”, Process Mining Workshops (ML4PM), ICPM 2022, https://doi.org/10.1007/978-3-031-27815-0_19
- Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt, “Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors”, Petri Nets Workshops (Algorithms and Theories for the Analysis of Event Data), Petri Nets 2022, https://ceur-ws.org/Vol-3167/paper1.pdf
- Alexander Stevens, Johannes De Smedt, Jari Peeperkorn, “Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring”, Process Mining Workshops (ML4PM), ICPM 2021, https://doi.org/10.1007/978-3-030-98581-3_15
- Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt, “Can deep neural networks learn process model structure? An assessment framework and analysis”, Process Mining Workshops (ML4PM), ICPM 2021, https://doi.org/10.1007/978-3-030-98581-3_10
- Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt, “Supervised Conformance Checking Using Recurrent Neural Network Classifiers”, Process Mining Workshops (ML4PM), ICPM 2020, https://doi.org/10.1007/978-3-030-72693-5_14
- Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt, “Conformance Checking Using Activity and Trace Embeddings”, Business Process Management Forum, BPM 2020, https://doi.org/10.1007/978-3-030-58638-6_7
Posters
- Jari Peeperkorn, Romain Dupuis, Giovanni Lapenta, “Forecasting geomagnetic storms using long short-term memory neural networks”, European Space Weather Week (ESWW) 2019, Liege, Belgium
In Submission
- Rafael Oyamada, Jari Peeperkorn, Johannes De Smedt, “Fine-Tuning Large Language Models for Multi-Task Predictive Process Monitoring”
- Yannis Bertrand, Martin Kabierski, Jari Peeperkorn, Seppe vanden Broucke, “How much can we improve process mining? A framework to analyse the impact of data quality on process discovery”
Preprints
- Lien Bosmans, Jari Peeperkorn, Alexandre Goossens, Giovanni Lugaresi, Johannes De Smedt, Jochen De Weerdt, “Dynamic and Scalable Data Preparation for Object-Centric Process Mining”, https://arxiv.org/abs/2410.00596
- Dirk Fahland, Marco Montali, Julian Lebherz, Wil M.P. van der Aalst, Maarten van Asseldonk, Peter Blank, Lien Bosmans, Marcus Brenscheidt, Claudio di Ciccio, Andrea Delgado, Daniel Calegari, Jari Peeperkorn, Eric Verbeek, Lotte Vugs, Moe Thandar Wynn, “Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) – Core Model, Design Space, and Lessons Learned”, https://arxiv.org/abs/2410.14495
Doctoral Booklet
- Jari Peeperkorn, “Novel Conformance Checking Methods and Validation Strategies for Deep Learning in Process Mining” (2023)