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Jana-Rebecca Rehse
Jana-Rebecca Rehse
Junior Professor, University of Mannheim
Bestätigte E-Mail-Adresse bei uni-mannheim.de
Titel
Zitiert von
Zitiert von
Jahr
Predicting process behaviour using deep learning
J Evermann, JR Rehse, P Fettke
Decision Support Systems 100, 129-140, 2017
4522017
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
1552017
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
1172023
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
982019
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
542023
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
522020
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
352017
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
292018
A reference data model for process-related user interaction logs
L Abb, JR Rehse
International Conference on Business Process Management, 57-74, 2022
272022
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
272022
Clustering business process activities for identifying reference model components
JR Rehse, P Fettke
Business Process Management Workshops: BPM 2018 International Workshops …, 2019
272019
XES tensorflow-Process prediction using the tensorflow deep-learning framework
J Evermann, JR Rehse, P Fettke
arXiv preprint arXiv:1705.01507, 2017
202017
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
192019
Process mining meets visual analytics: the case of conformance checking
JR Rehse, L Pufahl, M Grohs, LM Klein
arXiv preprint arXiv:2209.09712, 2022
182022
Eine Untersuchung der Potentiale automatisierter Abstraktionsansätze für Geschäftsprozessmodelle im Hinblick auf die induktive Entwicklung von Referenzprozessmodellen
JR Rehse, P Fettke, P Loos
142013
Trace Clustering for User Behavior Mining.
L Abb, C Bormann, H van der Aa, JR Rehse
ECIS, 2022
132022
Inductive reference model development: recent results and current challenges
JR Rehse, P Hake, P Fettke, P Loos
Informatik 2016, 739-752, 2016
122016
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
112022
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
102016
Supporting complaint management in the medical technology industry by means of deep learning
P Hake, JR Rehse, P Fettke
Business Process Management Workshops: BPM 2019 International Workshops …, 2019
92019
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