| Autoencoders for improving quality of process event logs HTC Nguyen, S Lee, J Kim, J Ko, M Comuzzi Expert Systems with Applications 131, 132-147, 2019 | 48 | 2019 |
| Explainable predictive process monitoring: a user evaluation W Rizzi, M Comuzzi, C Di Francescomarino, C Ghidini, S Lee, FM Maggi, ... arXiv preprint arXiv:2202.07760, 2022 | 9 | 2022 |
| A Window of Opportunity: Active Window Tracking for Mining Work Practices I Beerepoot, D Barenholz, S Beekhuis, J Gulden, S Lee, X Lu, S Overbeek, ... 2023 5th International Conference on Process Mining (ICPM), 57-64, 2023 | 5 | 2023 |
| Exploring the suitability of rule-based classification to provide interpretability in outcome-based process predictive monitoring S Lee, M Comuzzi, N Kwon Algorithms 15 (6), 187, 2022 | 5 | 2022 |
| The analysis of online event streams: Predicting the next activity for anomaly detection S Lee, X Lu, HA Reijers International Conference on Research Challenges in Information Science, 248-264, 2022 | 5 | 2022 |
| Continuous performance evaluation for business process outcome monitoring S Lee, M Comuzzi, X Lu International Conference on Process Mining, 237-249, 2021 | 2 | 2021 |
| Predicting outpatient process flows to minimise the cost of handling returning patients: A case study M Comuzzi, J Ko, S Lee Business Process Management Workshops: BPM 2019 International Workshops …, 2019 | 2 | 2019 |
| Measuring the Stability of Process Outcome Predictions in Online Settings S Lee, M Comuzzi, X Lu, HA Reijers 2023 5th International Conference on Process Mining (ICPM), 105-112, 2023 | | 2023 |
Marco ComuzziUlsan National Institute of Science and TechnologyDirección de correo verificada de unist.ac.kr
