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Jana-Rebecca Rehse
Jana-Rebecca Rehse
Junior Professor, University of Mannheim
Verified email at uni-mannheim.de
Title
Cited by
Cited by
Year
Predicting process behaviour using deep learning
J Evermann, JR Rehse, P Fettke
Decision Support Systems 100, 129-140, 2017
4152017
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
1452017
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
902019
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
732023
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
462020
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
272018
Clustering business process activities for identifying reference model components
JR Rehse, P Fettke
Business Process Management Workshops: BPM 2018 International Workshops …, 2019
262019
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
222022
XES tensorflow-Process prediction using the tensorflow deep-learning framework
J Evermann, JR Rehse, P Fettke
arXiv preprint arXiv:1705.01507, 2017
212017
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
172019
A reference data model for process-related user interaction logs
L Abb, JR Rehse
International Conference on Business Process Management, 57-74, 2022
162022
Eine Untersuchung der Potentiale automatisierter Abstraktionsansätze für Geschäftsprozessmodelle im Hinblick auf die induktive Entwicklung von Referenzprozessmodellen
JR Rehse, P Fettke, P Loos
142013
Inductive reference model development: recent results and current challenges
JR Rehse, P Hake, P Fettke, P Loos
Gesellschaft für Informatik eV, 2016
122016
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
102023
Process mining meets visual analytics: the case of conformance checking
JR Rehse, L Pufahl, M Grohs, LM Klein
arXiv preprint arXiv:2209.09712, 2022
102022
Trace Clustering for User Behavior Mining.
L Abb, C Bormann, H van der Aa, JR Rehse
ECIS, 2022
102022
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
92016
An execution-semantic approach to inductive reference model development
JR Rehse, P Fettke, P Loos
92016
Towards explainable process predictions for industry 4.0 in the DFKI-Smart-Lego-Factory. KI-Künstliche Intelligenz 33 (2), 181–187 (2019)
JR Rehse, N Mehdiyev, P Fettke
82019
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