| Analyzing business process anomalies using autoencoders T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Machine Learning 107, 1875-1893, 2018 | 73 | 2018 |
| BINet: multivariate business process anomaly detection using deep learning T Nolle, A Seeliger, M Mühlhäuser Business Process Management: 16th International Conference, BPM 2018, Sydney …, 2018 | 52 | 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 | 51 | 2016 |
| Binet: Multi-perspective business process anomaly classification T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Information Systems 103, 101458, 2022 | 47 | 2022 |
| 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 | 40 | 2015 |
| 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 | 38 | 2017 |
| DeepAlign: alignment-based process anomaly correction using recurrent neural networks T Nolle, A Seeliger, N Thoma, M Mühlhäuser Advanced Information Systems Engineering: 32nd International Conference …, 2020 | 18 | 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 | 18 | 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 | 12 | 2018 |
| Process compliance checking using taint flow analysis A Seeliger, T Nolle, B Schmidt, M Mühlhäuser | 8 | 2016 |
| Learning of process representations using recurrent neural networks A Seeliger, S Luettgen, T Nolle, M Mühlhäuser Advanced Information Systems Engineering: 33rd International Conference …, 2021 | 7 | 2021 |
| Case2vec: Advances in representation learning for business processes S Luettgen, A Seeliger, T Nolle, M Mühlhäuser Process Mining Workshops: ICPM 2020 International Workshops, Padua, Italy …, 2021 | 6 | 2021 |
| 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 | 6 | 2018 |
| A semantic browser for linked open data A Seeliger, H Paulheim | 6 | 2012 |
| A method for debugging process discovery pipelines to analyze the consistency of model properties C Klinkmüller, A Seeliger, R Müller, L Pufahl, I Weber Business Process Management: 19th International Conference, BPM 2021, Rome …, 2021 | 2 | 2021 |
| 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 |
| 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 | 2 | 2018 |
| 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 | 2 | 2016 |
| Inferring a Multi-perspective Likelihood Graph from Black-Box Next Event Predictors Y Gerlach, A Seeliger, 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 |
Max MühlhäuserProfessor of Computer Science, Technische Universität DarmstadtVerified email at informatik.tu-darmstadt.de
