| Analyzing business process anomalies using autoencoders T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Machine Learning 107 (11), 1875-1893, 2018 | 64 | 2018 |
| Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders T Nolle, A Seeliger, M Mühlhäuser International conference on discovery science, 442-456, 2016 | 45 | 2016 |
| BINet: multivariate business process anomaly detection using deep learning T Nolle, A Seeliger, M Mühlhäuser International Conference on Business Process Management, 271-287, 2018 | 39 | 2018 |
| Binet: Multi-perspective business process anomaly classification T Nolle, S Luettgen, A Seeliger, M Mühlhäuser Information Systems 103, 101458, 2022 | 35 | 2022 |
| Upgrading wireless home routers for enabling large-scale deployment of cloudlets C Meurisch, A Seeliger, B Schmidt, I Schweizer, F Kaup, M Mühlhäuser International conference on mobile computing, applications, and services, 12-29, 2015 | 34 | 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 | 33 | 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 | 12 | 2020 |
| ProcessExplorer: intelligent process mining guidance A Seeliger, A Sánchez Guinea, T Nolle, M Mühlhäuser International Conference on Business Process Management, 216-231, 2019 | 12 | 2019 |
| Finding structure in the unstructured: hybrid feature set clustering for process discovery A Seeliger, T Nolle, M Mühlhäuser International Conference on Business Process Management, 288-304, 2018 | 9 | 2018 |
| 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 |
| Process compliance checking using taint flow analysis A Seeliger, T Nolle, B Schmidt, M Mühlhäuser | 6 | 2016 |
| A semantic browser for linked open data A Seeliger, H Paulheim | 6 | 2012 |
| Case2vec: Advances in representation learning for business processes S Luettgen, A Seeliger, T Nolle, M Mühlhäuser International Conference on Process Mining, 162-174, 2020 | 4 | 2020 |
| 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 | 3 | 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 International Conference on Business Process Management, 230-242, 2017 | 2 | 2017 |
| 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 |
| 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 | 1 | 2021 |
| Extended synthetic event logs for multi-perspective trace clustering A Seeliger, S Lüttgen, M Mühlhäuser, T Nolle | | 2020 |
| Intelligent Computer-assisted Process Mining A Seeliger Technische Universität Darmstadt, 2020 | | 2020 |
Max MühlhäuserProfessor of Computer Science, Technische Universität DarmstadtVerified email at informatik.tu-darmstadt.de
