Publikationen
- Rehse, J.-R. und Fettke, P. (2019). A procedure model for situational reference model mining. Enterprise Modelling and Information Systems Architectures, 14, 1-42.
- 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.
- Rehse, J.-R., Dadashnia, S. und Fettke, P. (2018). Business process management for Industry 4.0 - Three application cases in the DFKI-Smart-Lego-Factory. Information Technology : it, 60, 133-141.
- Evermann, J., Rehse, J.-R. und Fettke, P. (2017). Predicting process behaviour using deep learning. Decision Support Systems : DSS, 100, 129-140.
- Rehse, J.-R., Fettke, P. und Loos, P. (2017). A graph-theoretic method for the inductive development of reference process models. Software & Systems Modeling : SoSyM, 16, 833-873.
- Berrang, A., Houy, C., Rehse, J.-R. und Fettke, P. (2020). Prozessorientierte Schulung von Einsatzkräften für robotergestützte Rettungsmissionen der Feuerwehr. In , Proceedings of the 15th International Conference on Wirtschaftsinformatik (WI2020) : March 9-11, Potsdam, Germany (S. ). , Universität Potsdam: Potsdam.
- Hussung, C., Rehse, J.-R., Houy, C. und Fettke, P. (2020). Entwicklung eines Referenzprozessmodells für Rettungseinsätze der Feuerwehr und Anwendung als Grundlage eines Prozessassistenzsystems. In , Entwicklungen, Chancen und Herausforderungen der Digitalisierung : Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik, WI2020 : March 9-11, Potsdam, Germany : Zentrale Tracks (S. 522-537). , GITO Verlag für Industrielle Informationstechnik und Organisation: Berlin.
- 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. In , Business Process Management Forum : BPM Forum 2020, Seville, Spain, September 13–18, 2020, proceedings (S. 71-87). , Springer International Publishing: Cham.
- Zandkarimi, F., Rehse, J.-R., Soudmand, P. und Höhle, H. (2020). A generic framework for trace clustering in process mining. In , 2nd International Conference on Process Mining (ICPM) : Virtual conference, 4-9 October 2020, Padua, Italy, proceedings (S. 177-184). , IEEE Computer Society: Los Alamitos, CA [u.a.].
- Hake, P., Rehse, J.-R. und Fettke, P. (2019). Supporting complaint management in the medical technology industry by means of deep learning. In , Business Process Management Workshops : BPM 2019 International Workshops, Vienna, Austria, September 1-6, 2019, Revised Selected Papers (S. 56-67). Lecture Notes in Business Information Processing, Springer International Publishing: Cham.
- Houy, C., Rehse, J.-R., Scheid, M. und Fettke, P. (2019). Model-based compliance in information systems - foundations, case description and data set of the MobIS-Challenge for students and doctoral candidates. In , Human practice, digital ecologies, our future : 14. Internationale Tagung Wirtschaftsinformatik (WI 2019) : Tagungsband (S. 1997-2010). , Universität Siegen: Siegen.
- 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, Springer International Publishing: Cham.
- Willms, C., Houy, C., Rehse, J.-R., Fettke, P. und Kruijff-Korbayová, I. (2019). Team communication processing and process analytics for supporting robot-assisted emergency response. In , 2019 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) : Würzburg, Germany, September 2-4, 2019 (S. 216-221). , IEEE: Piscataway, NJ.
- 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, RWTH: Aachen.
- Rehse, J.-R. und Fettke, P. (2018). Process mining crimes - a threat to the validity of process discovery evaluations. In , Business Process Management Forum : BPM Forum 2018, Sydney, NSW, Australia, September 9-14, 2018, Proceedings (S. 3-19). Lecture Notes in Business Information Processing, Springer International Publishing: Cham.
- Rehse, J.-R., Fettke, P. und Loos, P. (2018). Process Mining and the black swan: An empirical analysis of the influence of unobserved behavior on the quality of mined process models. In , Business Process Management Workshops : BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers (S. 256-268). Lecture Notes in Business Information Processing, Springer: Cham.
- 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, Springer: Cham.
- Evermann, J., Rehse, J.-R. und Fettke, P. (2017). XES Tensorflow - process prediction using the Tensorflow deep-learning framework. In , Advanced information systems engineering : 29th International Conference CAiSE 2017, Essen, Germany, June 12-16, 2017 : proceedings of CAiSE Forum and Doctoral Consortium papers (S. 41-48). CEUR Workshop Proceedings, RWTH: Aachen.
- Rehse, J.-R. und Fettke, P. (2017). Towards situational reference model mining - main idea, procedure model & case study. In , Towards thought leadership in digital transformation : 13. Internationale Tagung Wirtschaftsinformatik (WI 2017), Tagungsband (S. 271-285). , Institut für Wirtschaftsinformatik (IWI-HSG): St. Gallen.
- Evermann, J., Rehse, J.-R. und Fettke, P. (2016). A deep learning approach for predicting process behaviour at runtime. In , Business Process Management Workshops : BPM 2016 International Workshops, Rio de Janeiro, Brazil, September 19, 2016, Revised Papers (S. 327-338). Lecture Notes in Business Information Processing, Springer: Cham.
- Evermann, J., Rehse, J.-R. und Fettke, P. (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. In , 8th IEEE International Conference on Cloud Computing Technology and Science : CloudCom 2016 : 12-15 December 2016, Luxembourg City, Luxembourg : proceedings (S. 645-652). , IEEE: Piscataway, NJ.
- Rehse, J.-R. und Fettke, P. (2016). Mining reference process models from large instance data. In , Business Process Management Workshops : BPM 2016 International Workshops, Rio de Janeiro, Brazil, September 19, 2016, Revised Papers (S. 11-32). Lecture Notes in Business Information Processing, Springer: Cham.
- Rehse, J.-R., Fettke, P. und Loos, P. (2016). An execution-semantic approach to inductive reference models development. In , 24th European Conference on Information Systems, ECIS 2016 : Istanbul, Turkey, June 12-15, 2016 (S. Paper 80). , AISeL: Atlanta, GA.
- Rehse, J.-R., Hake, P., Fettke, P. und Loos, P. (2016). Inductive reference model development: Recent results and current challenges. In , Informatik 2016 : Tagung vom 26.-30. September 2016 in Klagenfurt (S. 739-752). GI-Edition : Lecture Notes in Informatics. Proceedings, Geellschaft für Informatik : Köllen: Bonn.
- Gutermuth, O., Lahann, J., Rehse, J.-R., Scheid, M., Schuhmann, S., Stephan, S. und Fettke, P. (2019). Efficient and compliant purchase order handling : A contribution to BPI Challenge 2019. 9th International Business Process Intelligence Challenge, BPI Challenge 2019, Aachen, Germany.
- Rehse, J. (2016). Reference model mining - concepts, methods, applications. 18th IEEE Conference on Business Informatics CBI 2016 Doctoral Consortium, Paris, France.