Rehse, J.-R., Mehdiyev, N. und Fettke, P. (2019). Towards explainable process predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory.
Künstliche Intelligenz : KI ; Forschung, Entwicklung, Erfahrungen ; Organ des Fachbereichs 1 Künstliche Intelligenz der Gesellschaft für Informatik e.V., GI / Fachbereich 1 der Gesellschaft für Informatik e.V, 33, 181-187.
Rebmann, A., Rehse, J.-R., Pinter, M., Schnaubelt, M., Daun, K. und Fettke, P. (2020). IoT-based task recognition for process assistance in human-robot disaster response.
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Zandkarimi, F., Rehse, J.-R., Soudmand, P. und Höhle, H. (2020). A generic framework for trace clustering in process mining.
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Hake, P., Rehse, J.-R. und Fettke, P. (2019). Supporting complaint management in the medical technology industry by means of deep learning.
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Rehse, J.-R. und Fettke, P. (2019). Clustering business process activities for identifying reference model components.
In , Business Process Management Workshops : BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers (S. 5-17). Lecture Notes in Business Information Processing,
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Rehse, J. (2018). Situational reference model mining.
In , CAiSE-DC 2018 : Proceedings of the Doctoral Consortium, papers presented at the 30th International Conference on Advanced Information Systems Engineering (CAiSE 2018) Tallinn, Estonia, June 11-15, 2018 (S. 28-36). CEUR Workshop Proceedings,
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Rehse, J.-R. und Fettke, P. (2018). Process mining crimes - a threat to the validity of process discovery evaluations.
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Tenschert, J., Rehse, J.-R., Fettke, P. und Lenz, R. (2018). Speech acts in actual processes: Evaluation of interfaces and triggers in ITIL.
In , Business Process Management Workshops : BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers (S. 348-360). Lecture Notes in Business Information Processing,
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Evermann, J., Rehse, J.-R. und Fettke, P. (2017). XES Tensorflow - process prediction using the Tensorflow deep-learning framework.
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Evermann, J., Rehse, J.-R. und Fettke, P. (2016). A deep learning approach for predicting process behaviour at runtime.
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Rehse, J.-R. und Fettke, P. (2016). Mining reference process models from large instance data.
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Rehse, J.-R., Hake, P., Fettke, P. und Loos, P. (2016). Inductive reference model development: Recent results and current challenges.
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Geellschaft für Informatik : Köllen: Bonn.
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