| An Anomaly Detection Technique for Business Processes based on Extended Dynamic Bayesian Networks S Pauwels, T Calders SAC, 2019 | 33 | 2019 |
| Incremental predictive process monitoring: The next activity case S Pauwels, T Calders International Conference on Business Process Management, 123-140, 2021 | 31 | 2021 |
| Bayesian network based predictions of business processes S Pauwels, T Calders Business Process Management Forum: BPM Forum 2020, Seville, Spain, September …, 2020 | 29 | 2020 |
| Detecting anomalies in hybrid business process logs S Pauwels, T Calders ACM SIGAPP Applied Computing Review 19 (2), 18-30, 2019 | 16 | 2019 |
| Detecting and explaining drifts in yearly grant applications S Pauwels, T Calders arXiv preprint arXiv:1809.05650, 2018 | 11 | 2018 |
| Interactive and manual construction of classification trees S Pauwels, S Moens, B Goethals BENELEARN 2014 81, 2014 | 6 | 2014 |
| Mining multi-dimensional complex log data S Pauwels, T Calders Proceedings of Benelearn 2016, 2016 | 4 | 2016 |
| Applying machine learning in business process monitoring S Pauwels University of Antwerp, 2022 | | 2022 |
| ACD2: a tool to interactively explore Business Process Logs S Pauwels, T Calders CEUR workshop proceedings, 129-133, 2019 | | 2019 |
Toon CaldersUniversity of AntwerpVerified email at uantwerpen.be
