| Predicting process behaviour using deep learning J Evermann, JR Rehse, P Fettke Decision Support Systems 100, 129-140, 2017 | 437 | 2017 |
| A deep learning approach for predicting process behaviour at runtime J Evermann, JR Rehse, P Fettke Business Process Management Workshops: BPM 2016 International Workshops, Rio …, 2017 | 150 | 2017 |
| AI-augmented business process management systems: a research manifesto M Dumas, F Fournier, L Limonad, A Marrella, M Montali, JR Rehse, ... ACM Transactions on Management Information Systems 14 (1), 1-19, 2023 | 96 | 2023 |
| Towards explainable process predictions for industry 4.0 in the dfki-smart-lego-factory JR Rehse, N Mehdiyev, P Fettke KI-Künstliche Intelligenz 33, 181-187, 2019 | 94 | 2019 |
| A generic framework for trace clustering in process mining F Zandkarimi, JR Rehse, P Soudmand, H Hoehle 2020 2nd International Conference on Process Mining (ICPM), 177-184, 2020 | 48 | 2020 |
| Large language models can accomplish business process management tasks M Grohs, L Abb, N Elsayed, JR Rehse International Conference on Business Process Management, 453-465, 2023 | 39 | 2023 |
| A graph-theoretic method for the inductive development of reference process models JR Rehse, P Fettke, P Loos Software & Systems Modeling 16 (3), 833-873, 2017 | 35 | 2017 |
| Business process management for Industry 4.0–Three application cases in the DFKI-Smart-Lego-Factory JR Rehse, S Dadashnia, P Fettke IT-Information Technology 60 (3), 133-141, 2018 | 29 | 2018 |
| Clustering business process activities for identifying reference model components JR Rehse, P Fettke Business Process Management Workshops: BPM 2018 International Workshops …, 2019 | 28 | 2019 |
| A reference data model for process-related user interaction logs L Abb, JR Rehse International Conference on Business Process Management, 57-74, 2022 | 27 | 2022 |
| Uncovering object-centric data in classical event logs for the automated transformation from XES to OCEL A Rebmann, JR Rehse, H van der Aa International Conference on Business Process Management, 379-396, 2022 | 26 | 2022 |
| XES tensorflow-Process prediction using the tensorflow deep-learning framework J Evermann, JR Rehse, P Fettke arXiv preprint arXiv:1705.01507, 2017 | 20 | 2017 |
| Team communication processing and process analytics for supporting robot-assisted emergency response C Willms, C Houy, JR Rehse, P Fettke, I Kruijff-Korbayová 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics …, 2019 | 19 | 2019 |
| Eine Untersuchung der Potentiale automatisierter Abstraktionsansätze für Geschäftsprozessmodelle im Hinblick auf die induktive Entwicklung von Referenzprozessmodellen JR Rehse, P Fettke, P Loos | 14 | 2013 |
| Process mining meets visual analytics: the case of conformance checking JR Rehse, L Pufahl, M Grohs, LM Klein arXiv preprint arXiv:2209.09712, 2022 | 13 | 2022 |
| Trace Clustering for User Behavior Mining. L Abb, C Bormann, H van der Aa, JR Rehse ECIS, 2022 | 13 | 2022 |
| Inductive reference model development: recent results and current challenges JR Rehse, P Hake, P Fettke, P Loos Informatik 2016, 739-752, 2016 | 12 | 2016 |
| Process discovery from event stream data in the cloud-A scalable, distributed implementation of the flexible heuristics miner on the Amazon kinesis cloud infrastructure J Evermann, JR Rehse, P Fettke 2016 IEEE International Conference on Cloud Computing Technology and Science …, 2016 | 10 | 2016 |
| SAP Signavio Academic Models: a large process model dataset D Sola, C Warmuth, B Schäfer, P Badakhshan, JR Rehse, T Kampik International Conference on Process Mining, 453-465, 2022 | 9 | 2022 |
| Process mining and the black swan: an empirical analysis of the influence of unobserved behavior on the quality of mined process models JR Rehse, P Fettke, P Loos Business Process Management Workshops: BPM 2017 International Workshops …, 2018 | 9 | 2018 |
