Niek Tax
TitleCited byYear
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
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
A cross-benchmark comparison of 87 learning to rank methods
N Tax, S Bockting, D Hiemstra
Information processing & management 51 (6), 757-772, 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
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
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
Predicting The Dutch Football Competition Using Public Data: A Machine Learning Approach
N Tax, Y Joustra
2014, 2014
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
F Mannhardt, N Tax
International Working Conference on Business Process Modeling, Development …, 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
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
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
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
An interdisciplinary comparison of sequence modeling methods for next-element prediction
N Tax, I Teinemaa, SJ van Zelst
arXiv preprint arXiv:1811.00062, 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
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
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
Interest-Driven Discovery of Local Process Models
N Tax, B Dalmas, N Sidorova, WMP van der Aalst, S Norre
Information Systems, 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
Ranking learning-to-rank methods
D Hiemstra, N Tax, S Bockting
Proceedings of the International Workshop on Learning Next Generation …, 2017
Scaling Learning to Rank to Big Data: Using MapReduce to Parallelise Learning to Rank
N Tax
University of Twente, 2014
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