| Analyzing business process anomalies using autoencoders T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Machine Learning 107, 1875-1893, 2018 | 110 | 2018 |
| Binet: Multi-perspective business process anomaly classification T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Information Systems 103, 101458, 2022 | 87 | 2022 |
| BINet: multivariate business process anomaly detection using deep learning T Nolle, A Seeliger, M Mühlhäuser International Conference on Business Process Management, 271-287, 2018 | 82 | 2018 |
| Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders T Nolle, A Seeliger, M Mühlhäuser Discovery Science: 19th International Conference, DS 2016, Bari, Italy …, 2016 | 68 | 2016 |
| Detecting concept drift in processes using graph metrics on process graphs A Seeliger, T Nolle, M Mühlhäuser Proceedings of the 9th Conference on Subject-Oriented Business Process …, 2017 | 60 | 2017 |
| DeepAlign: alignment-based process anomaly correction using recurrent neural networks T Nolle, A Seeliger, N Thoma, M Mühlhäuser International conference on advanced information systems engineering, 319-333, 2020 | 28 | 2020 |
| ProcessExplorer: intelligent process mining guidance A Seeliger, A Sánchez Guinea, T Nolle, M Mühlhäuser Business Process Management: 17th International Conference, BPM 2019, Vienna …, 2019 | 27 | 2019 |
| Finding structure in the unstructured: hybrid feature set clustering for process discovery A Seeliger, T Nolle, M Mühlhäuser Business Process Management: 16th International Conference, BPM 2018, Sydney …, 2018 | 18 | 2018 |
| Learning of process representations using recurrent neural networks A Seeliger, S Luettgen, T Nolle, M Mühlhäuser International Conference on Advanced Information Systems Engineering, 109-124, 2021 | 14 | 2021 |
| Case2vec: Advances in representation learning for business processes S Luettgen, A Seeliger, T Nolle, M Mühlhäuser International Conference on Process Mining, 162-174, 2020 | 12 | 2020 |
| Process compliance checking using taint flow analysis A Seeliger, T Nolle, B Schmidt, M Mühlhäuser | 10 | 2016 |
| Process explorer: an interactive visual recommendation system for process mining A Seeliger, T Nolle, M Mühlhäuser KDD Workshop on Interactive Data Exploration and Analytics, 2018 | 9 | 2018 |
| Inferring a multi-perspective likelihood graph from black-box next event predictors Y Gerlach, A Seeliger, T Nolle, M Mühlhäuser International Conference on Advanced Information Systems Engineering, 19-35, 2022 | 5 | 2022 |
| Data-driven detection of congestion-affected roads T Nolle, I Schweizer, F Janssen Technical Report TUD-KE-2014-02, 2014 | 4 | 2014 |
| Capturing daily student life by recognizing complex activities using smartphones C Meurisch, A Gogel, B Schmidt, T Nolle, F Janssen, I Schweizer, ... Proceedings of the 14th EAI International Conference on Mobile and …, 2017 | 3 | 2017 |
| Process Explorer: Interactive Visual Exploration of Event Logs with Analysis Guidance A Seeliger, R Maximilian, T Nolle, M Mühlhäuser, MB Andrea, P Artem, ... Proceedings of the 1st International Conference on Process Mining, Aachen …, 2019 | 2 | 2019 |
| Process learning for autonomous process anomaly correction T Nolle Learning 1, P4, 2003 | 2 | 2003 |
| Inferring a Multi-perspective Likelihood Graph from Black-Box Next Event Predictors T Nolle, M Mühlhäuser Advanced Information Systems Engineering: 34th International Conference …, 2022 | | 2022 |
| Extended synthetic event logs for multi-perspective trace clustering A Seeliger, S Lüttgen, M Mühlhäuser, T Nolle | | 2020 |
| Synthetic event logs for multi-perspective trace clustering A Seeliger, T Nolle, M Mühlhäuser | | 2018 |
Max MühlhäuserProfessor of Computer Science, Technische Universität DarmstadtVerified email at informatik.tu-darmstadt.de
