| Analyzing business process anomalies using autoencoders T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Machine Learning 107, 1875-1893, 2018 | 103 | 2018 |
| Binet: Multi-perspective business process anomaly classification T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Information Systems 103, 101458, 2022 | 80 | 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 | 76 | 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 | 65 | 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 | 56 | 2017 |
| Upgrading wireless home routers for enabling large-scale deployment of cloudlets C Meurisch, A Seeliger, B Schmidt, I Schweizer, F Kaup, M Mühlhäuser Mobile Computing, Applications, and Services: 7th International Conference …, 2015 | 45 | 2015 |
| 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 | 27 | 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 | 17 | 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 | 12 | 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 | 11 | 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 | 8 | 2018 |
| A semantic browser for linked open data A Seeliger, H Paulheim | 6 | 2012 |
| What belongs together comes together: Activity-centric document clustering for information work A Seeliger, B Schmidt, I Schweizer, M Mühlhäuser Proceedings of the 21st International Conference on Intelligent User …, 2016 | 5 | 2016 |
| 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 | 3 | 2022 |
| A method for debugging process discovery pipelines to analyze the consistency of model properties C Klinkmüller, A Seeliger, R Müller, L Pufahl, I Weber International Conference on Business Process Management, 65-84, 2021 | 3 | 2021 |
| Can we find better process models? process model improvement using motif-based graph adaptation A Seeliger, M Stein, M Mühlhäuser Business Process Management Workshops: BPM 2017 International Workshops …, 2018 | 3 | 2018 |
| ProcessExplorer: Interactive Visual Exploration of Event Logs with Analysis Guidance A Seeliger, M Ratzke, T Nolle, M Mühlhäuser International Conference on Process Mining - ICPM Demo Track 2019, 24-27, 2019 | 2 | 2019 |
| Intelligent Computer-assisted Process Mining A Seeliger Dissertation, Darmstadt, Technische Universität Darmstadt, 2020, 2020 | 1 | 2020 |
Max MühlhäuserProfessor of Computer Science, Technische Universität DarmstadtDirección de correo verificada de informatik.tu-darmstadt.de
