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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
2902017
A deep learning approach for predicting process behaviour at runtime
J Evermann, JR Rehse, P Fettke
International Conference on Business Process Management, 327-338, 2016
1062016
Towards explainable process predictions for industry 4.0 in the dfki-smart-lego-factory
JR Rehse, N Mehdiyev, P Fettke
KI-Künstliche Intelligenz 33 (2), 181-187, 2019
522019
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
322017
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
182018
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
172020
XES tensorflow-Process prediction using the tensorflow deep-learning framework
J Evermann, JR Rehse, P Fettke
arXiv preprint arXiv:1705.01507, 2017
162017
Eine Untersuchung der Potentiale automatisierter Abstraktionsansätze für Geschäftsprozessmodelle im Hinblick auf die induktive Entwicklung von Referenzprozessmodellen
JR Rehse, P Fettke, P Loos
142013
Clustering business process activities for identifying reference model components
JR Rehse, P Fettke
International Conference on Business Process Management, 5-17, 2018
132018
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
122019
Inductive reference model development: recent results and current challenges
JR Rehse, P Hake, P Fettke, P Loos
Informatik 2016, 2016
112016
An execution-semantic approach to inductive reference model development
JR Rehse, P Fettke, P Loos
92016
Augmented business process management systems: a research manifesto
M Dumas, F Fournier, L Limonad, A Marrella, M Montali, JR Rehse, ...
arXiv preprint arXiv:2201.12855, 2022
62022
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
62019
Towards situational reference model mining-main idea, procedure model & case study
JR Rehse, P Fettke
62017
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
62016
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
International Conference on Business Process Management, 256-268, 2017
52017
Mining reference process models from large instance data
JR Rehse, P Fettke
International Conference on Business Process Management, 11-22, 2016
52016
Entwicklung eines Referenzprozessmodells für Rettungseinsätze der Feuerwehr und Anwendung als Grundlage eines Prozessassistenzsystems.
C Hussung, JR Rehse, C Houy, P Fettke
Wirtschaftsinformatik (Zentrale Tracks), 522-537, 2020
42020
Supporting complaint management in the medical technology industry by means of deep learning
P Hake, JR Rehse, P Fettke
International conference on Business Process Management, 56-67, 2019
42019
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