| Learning when to treat business processes: Prescriptive process monitoring with causal inference and reinforcement learning ZD Bozorgi, M Dumas, ML Rosa, A Polyvyanyy, M Shoush, I Teinemaa International Conference on Advanced Information Systems Engineering, 364-380, 2023 | 7 | 2023 |
| Prescriptive process monitoring based on causal effect estimation Z Dasht Bozorgi, I Teinemaa, M Dumas, M La Rosa, A Polyvyanyy | | 2023 |
| Prescriptive process monitoring based on causal effect estimation ZD Bozorgi, I Teinemaa, M Dumas, M La Rosa, A Polyvyanyy Information Systems 116, 102198, 2023 | 16 | 2023 |
| Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning Z Dasht Bozorgi, M Dumas, M La Rosa, A Polyvyanyy, M Shoush, ... arXiv e-prints, arXiv: 2303.03572, 2023 | | 2023 |
| Encoding resource experience for predictive process monitoring J Kim, M Comuzzi, M Dumas, FM Maggi, I Teinemaa Decision Support Systems 153, 113669, 2022 | 26 | 2022 |
| 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 64 (2), 559-587, 2022 | 54 | 2022 |
| 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 | 50 | 2021 |
| Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction Z Dasht Bozorgi, I Teinemaa, M Dumas, M La Rosa, A Polyvyanyy arXiv e-prints, arXiv: 2105.07111, 2021 | | 2021 |
| Summary of tutorials at the Web Conference 2021 R West, S Bhagat, P Groth, M Zitnik, FM Couto, P Lisena, ... Companion Proceedings of the Web Conference 2021, 727-733, 2021 | 3 | 2021 |
| 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 | 47 | 2021 |
| Uplift modeling: from causal inference to personalization I Teinemaa, J Albert, D Goldenberg Companion Proceedings of the Web Conference, 2021 | 5 | 2021 |
| 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 | 65 | 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 | 67 | 2020 |
| Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs Z Dasht Bozorgi, I Teinemaa, M Dumas, M La Rosa, A Polyvyanyy arXiv e-prints, arXiv: 2009.01561, 2020 | | 2020 |
| 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 | 193 | 2019 |
| Predictive and Prescriptive Monitoring of Business Process Outcomes. I Teinemaa, B Depaire BPM (PhD/Demos), 15-19, 2019 | 8 | 2019 |
| 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 | 347 | 2019 |
| 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 | 57 | 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 | 54 | 2018 |
| Automatic Playlist Continuation through a Composition of Collaborative Filters I Teinemaa, N Tax, C Bentes arXiv preprint arXiv:1808.04288, 2018 | 2 | 2018 |
Marlon DumasProfessor at University of Tartu | Chief Product Officer at ApromoreVerified email at ut.ee