| Outcome-oriented predictive process monitoring: review and benchmark I Teinemaa, M Dumas, M La Rosa, FM Maggi ACM Transactions on Knowledge Discovery in Data 13 (2), 17:1-17:57, 2019 | 161 | 2019 |
| Clustering-based predictive process monitoring C Di Francescomarino, M Dumas, FM Maggi, I Teinemaa IEEE transactions on services computing 12 (6), 896-909, 2016 | 146 | 2016 |
| Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring I Verenich, M Dumas, ML Rosa, FM Maggi, I Teinemaa ACM Transactions on Intelligent Systems and Technology (TIST) 10 (4), 1-34, 2019 | 93 | 2019 |
| Predictive business process monitoring with structured and unstructured data I Teinemaa, M Dumas, FM Maggi, CD Francescomarino International Conference on Business Process Management, 401-417, 2016 | 81 | 2016 |
| Semantics and analysis of DMN decision tables D Calvanese, M Dumas, Ü Laurson, FM Maggi, M Montali, I Teinemaa International Conference on Business Process Management, 217-233, 2016 | 53 | 2016 |
| Semantics, analysis and simplification of DMN decision tables D Calvanese, M Dumas, Ü Laurson, FM Maggi, M Montali, I Teinemaa Information Systems 78, 112-125, 2018 | 41 | 2018 |
| Temporal Stability in Predictive Process Monitoring I Teinemaa, M Dumas, A Leontjeva, FM Maggi Data Mining and Knowledge Discovery 32 (5), 1306–1338, 2018 | 34 | 2018 |
| Alarm-Based Prescriptive Process Monitoring I Teinemaa, N Tax, M de Leoni, M Dumas, FM Maggi International Conference on Business Process Management 329, 91-107, 2018 | 27 | 2018 |
| An interdisciplinary comparison of sequence modeling methods for next-element prediction N Tax, I Teinemaa, SJ van Zelst Software and Systems Modeling 19 (6), 1345-1365, 2020 | 23 | 2020 |
| Process mining meets causal machine learning: Discovering causal rules from event logs ZD Bozorgi, I Teinemaa, M Dumas, M La Rosa, A Polyvyanyy 2020 2nd International Conference on Process Mining (ICPM), 129-136, 2020 | 23 | 2020 |
| An interdisciplinary comparison of sequence modeling methods for next-element prediction N Tax, I Teinemaa, SJ van Zelst arXiv preprint arXiv:1811.00062, 2018 | 19 | 2018 |
| Personalization in Practice: Methods and Applications D Goldenberg, K Kofman, J Albert, S Mizrachi, A Horowitz, I Teinemaa Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021 | 18 | 2021 |
| Fire now, fire later: alarm-based systems for prescriptive process monitoring SA Fahrenkrog-Petersen, N Tax, I Teinemaa, M Dumas, M Leoni, ... Knowledge and Information Systems, 1-29, 2021 | 14 | 2021 |
| BPIC 2015: Diagnostics of building permit application process in dutch municipalities I Teinemaa, A Leontjeva, KO Masing BPI Challenge Report 72, 2015 | 13 | 2015 |
| A ProM Operational Support Provider for Predictive Monitoring of Business Processes. M Federici, W Rizzi, C Di Francescomarino, M Dumas, C Ghidini, ... BPM (Demos), 1-5, 2015 | 9 | 2015 |
| Prescriptive process monitoring for cost-aware cycle time reduction ZD Bozorgi, I Teinemaa, M Dumas, M La Rosa, A Polyvyanyy 2021 3rd International Conference on Process Mining (ICPM), 96-103, 2021 | 8 | 2021 |
| An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models N Tax, SJ Zelst, I Teinemaa Enterprise, Business-Process and Information Systems Modeling, 165-180, 2018 | 8 | 2018 |
| Community-based prediction of activity change in Skype I Teinemaa, A Leontjeva, M Dumas, R Kikas 2015 IEEE/ACM International Conference on Advances in Social Networks …, 2015 | 7 | 2015 |
| Predictive and Prescriptive Monitoring of Business Process Outcomes. I Teinemaa, B Depaire BPM (PhD/Demos), 15-19, 2019 | 4 | 2019 |
| Uplift Modeling: From Causal Inference to Personalization I Teinemaa, J Albert, D Goldenberg WebConf, 2021 | 1 | 2021 |
Marlon DumasProfessor at University of Tartu | Co-founder at ApromoreVerified email at ut.ee
