| Predictive business process monitoring with LSTM neural networks N Tax, I Verenich, M La Rosa, M Dumas Proceedings of the International Conference on Advanced Information Systems …, 2017 | 148 | 2017 |
| Mining local process models N Tax, N Sidorova, R Haakma, WMP van der Aalst Journal of Innovation in Digital Ecosystems 3 (2), 183-196, 2016 | 63 | 2016 |
| A cross-benchmark comparison of 87 learning to rank methods N Tax, S Bockting, D Hiemstra Information processing & management 51 (6), 757-772, 2015 | 58 | 2015 |
| Event abstraction for process mining using supervised learning techniques N Tax, N Sidorova, R Haakma, WMP van der Aalst Proceedings of the SAI Intelligent Systems Conference, 2016 | 48 | 2016 |
| The Imprecisions of Precision Measures in Process Mining N Tax, X Lu, N Sidorova, D Fahland, WMP van der Aalst Information Processing Letters 135, 1-8, 2018 | 34 | 2018 |
| Predicting The Dutch Football Competition Using Public Data: A Machine Learning Approach N Tax, Y Joustra 2014, 2014 | 26 | 2014 |
| Heuristic approaches for generating local process models through log projections N Tax, N Sidorova, WMP van der Aalst, R Haakma Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2016 | 25 | 2016 |
| Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models F Mannhardt, N Tax International Working Conference on Business Process Modeling, Development …, 2017 | 17 | 2017 |
| Mining Process Model Descriptions of Daily Life through Event Abstraction N Tax, N Sidorova, R Haakma, WMP van der Aalst Intelligent Systems and Applications 751, 2017 | 16 | 2017 |
| Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities N Tax, N Sidorova, WMP van der Aalst Journal of Intelligent Information Systems, 1-33, 2017 | 16 | 2017 |
| Log-based evaluation of label splits for process models N Tax, N Sidorova, R Haakma, WMP van der Aalst Procedia Computer Science 96, 63-72, 2016 | 12 | 2016 |
| On generation of time-based label refinements N Tax, E Alasgarov, N Sidorova, R Haakma Proceedings of the 25th International Workshop on Concurrency, Specification …, 2016 | 11 | 2016 |
| An interdisciplinary comparison of sequence modeling methods for next-element prediction N Tax, I Teinemaa, SJ van Zelst Software and Systems Modeling, 2018 | 10 | 2018 |
| On the Use of Hierarchical Subtrace Mining for Efficient Local Process Model Mining N Tax, L Genga, N Zannone Proceedings of the International Symposium on Data-driven Process Discovery …, 2017 | 9 | 2017 |
| Heuristics for High-Utility Local Process Model Mining B Dalmas, N Tax, S Norre Proceedings of the International Workshop on Algorithms & Theories for the …, 2017 | 7 | 2017 |
| Alarm-based prescriptive process monitoring I Teinemaa, N Tax, M de Leoni, M Dumas, FM Maggi International Conference on Business Process Management, 91-107, 2018 | 6 | 2018 |
| Interest-Driven Discovery of Local Process Models N Tax, B Dalmas, N Sidorova, WMP van der Aalst, S Norre Information Systems, 2018 | 6 | 2018 |
| Human Activity Prediction in Smart Home Environments with LSTM Neural Networks N Tax Proceedings of the International Conference on Intelligent Environments, 2018 | 4 | 2018 |
| Mining Local Process Models with Constraints Efficiently: Applications to the Analysis of Smart Home Data N Tax, N Sidorova, R Haakma, WMP van der Aalst Proceedings of the International Conference on Intelligent Environments, 2018 | 3 | 2018 |
| LocalProcessModelDiscovery: Bringing Petri Nets to the Pattern Mining World N Tax, N Sidorova, WMP van der Aalst, R Haakma Application and Theory of Petri Nets and Concurrency, 2018 | 3 | 2018 |
Wil van der AalstRWTH Aachen UniversityVerified email at pads.rwth-aachen.de
