Prof. Dr. Jana-Rebecca Rehse

Junior Professorship for Management Analytics

University of Mannheim
Assistant Professor of Management Analytics
L 15, 1–6 – Room 413
68161 Mannheim

Phone: +49 621 181-1766
E-mail: rehseuni-mannheim.de

Credit: Rike Allendoerfer

Research Focus

Data-driven business process management by means of process mining and machine learning, particularly

User Behavior Mining (UBM)
Applicability of Process Mining in Business Practice
Evaluation Methods in Process Mining

Courses

IS 515: Process Management and Analytics
IS 752: Master-Seminar

Awards and Prices

2022	Runner-Up Best Paper Award, International Conference on Business Process Management
2022	Member of the MISQ Scholarly Development Academy
2020	“Preis der Förderer”, Institute for Empirical Economic Research, Saarland University
2020	“Dr.-Eduard-Martin-Preis”, for the best dissertations at Saarland University
2016	Best Paper Nomination European Conference on Information Systems
2014	DAAD full scholarship “fitWeltweit” for theses abroad

Biography

since 2021	Lecturer for “Process Management Analytics”, Saarland University, Saarbrücken, Germany
since 2020	Junior Professor for Management Analytics, Faculty of Business Administration, University of Mannheim, Germany
2015–2020	Research Assistant, Institute for Information Systems, German Research Center for Artificial Intelligence, Saarbrücken, Germany
2015–2019	Doctorate in Business Administration (grade: summa cum laude), Saarland University, Saarbrücken, Germany Topic: “Leveraging Artificial Intelligence for Business Process Management – A Contribution to Reference Model Mining, Predictive Process Monitoring, and Process Discovery”
2018–2019	Lecturer for “Scientific Work”, University of Applied Sciences of Saarland, Saarbrücken, Germany
2012–2015	Master of Science in Information Systems, Saarland University, Saarbrücken, Germany
2014	Visiting Research Scholar, Center of Business Process Innovation, Howe School of Technology Management, Stevens Institute of Technology, Hoboken, New Jersey, USA
2010–2012	Bachelor of Science in Business Information Systems, Saarland University, Saarbrücken, Germany

Rehse, J.-R., Abb, L., Berg, G., Bormann, C., Kampik, T. and Warmuth, C. (2024). User behavior mining: A research agenda. Business & Information Systems Engineering : BISE, 1–18.
Dumas, M., Fournier, F., Limonad, L., Marrella, A., Montali, M., Rehse, J.-R., Accorsi, R., Calvanese, D., De Giacomo, G., Fahland, D., Gal, A., La Rosa, M., Völzer, H. and Weber, I. (2023). AI-augmented business process management systems: a research manifesto. ACM Transactions on Management Information Systems, 14, 1–19.
Hake, P., Rehse, J.-R. and Fettke, P. (2021). Toward automated support of complaint handling processes: an application in the medical technology industry. Journal on Data Semantics : JoDS, 10, 41–56.
Rehse, J.-R. and 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. and Fettke, P. (2019). Towards explainable process predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory. Künstliche Intelligenz : KI, 33, 181–187.
Rehse, J.-R., Dadashnia, S. and 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. and Fettke, P. (2017). Predicting process behaviour using deep learning. Decision Support Systems : DSS, 100, 129–140.
Rehse, J.-R., Fettke, P. and Loos, P. (2017). A graph-theoretic method for the inductive development of reference process models. Software & Systems Modeling : SoSyM, 16, 833–873.

Rehse, J.-R., Pufahl, L., Grohs, M. and Klein, L.-M. (2023). Process mining meets visual analytics: the case of conformance checking. In , Proceedings of the 56th Annual Hawaii International Conference on System Sciences : January 3–6, 2023 (S. 5452-5461). , University of Hawaii at Manoa: Honolulu, HI.
Sola, D., Warmuth, C., Schäfer, B., Badakhshan, P., Rehse, J.-R. and Kampik, T. (2023). SAP Signavio Academic Models: A large process model dataset. In , Process Mining Workshops : ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers (S. 453–465). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u.a.].
Abb, L., Bormann, C., der Aa, H. and Rehse, J.-R. (2022). Trace clustering for user behavior mining. In , Proceedings of the 30th European Conference on Information Systems (ECIS): Timișoara, Romania, June 18–24, 2022 (S. 1–17). European Conference on Information Systems (ECIS) : Research Papers, AISeL: Atlanta, GA.
Abb, L. and Rehse, J.-R. (2022). A reference data model for process-related user interaction logs. In , Business Process Management : 20th International Conference, BPM 2022, Münster, Germany, September 11–16, 2022, proceedings (S. 57–74). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Agostinelli, S., Marrella, A., Abb, L. and Rehse, J.-R. (2022). Mastering robotic process automation with process mining. In , Business Process Management : 20th International Conference, BPM 2022, Münster, Germany, September 11–16, 2022, proceedings (S. 47–53). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Rebmann, A., Rehse, J.-R. and der Aa, H. (2022). Uncovering object-centric data in classical event logs for the automated transformation from XES to OCEL. In , Business Process Management : 20th International Conference, BPM 2022, Münster, Germany, September 11–16, 2022, proceedings (S. 379–396). Lecture Notes in Computer Science, Springer: Berlin [u.a.].
Fellmann, M., Laue, R., Lantow, B. and Rehse, J.-R. (2021). Stand, Herausforderungen und Impulse des Geschäftsprozessmanagements. In , Informatik 2020 – Back to the future : 50. Jahrestagung der Gesellschaft für Informatik vom 28. September – 2. Oktober 2020, virtual (S. 587–589). GI-Edition : Lecture Notes in Informatics. Proceedings, Ges. für Informatik: Bonn.
Laue, R., Rehse, J.-R. and Schoormann, T. (2021). 7. Workshop zum Stand und den Herausforderungen des Geschäftsprozessmanagements. In , Informatik 2021 – Computer science & sustainability : 27. September – 1. Oktober 2021, Berlin (S. 1499-1500). GI-Edition : Lecture Notes in Informatics. Proceedings, Ges. für Informatik: Bonn.
Pufahl, L. and Rehse, J.-R. (2021). Conformance checking with regulations – a research agenda. In , EMISA 2021: Enterprise Modeling and Information Systems Architectures 2021 : Proceedings of the 11th International Workshop on Enterprise Modeling and Information Systems Architectures : Kiel, Germany, May 20–21, 2021 (S. 24–29). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Striewe, M., Houy, C., Rehse, J.-R., Ullrich, M., Fettke, P., Schaper, N. and Oberweis, A. (2021). Towards an automated assessment of graphical (business process) modelling competences. In , Informatik 2020 – Back to the future : 50. Jahrestagung der Gesellschaft fr Informatik vom 28. September – 2. Oktober 2020, virtual (S. 665–670). GI-Edition : Lecture Notes in Informatics. Proceedings, Ges. für Informatik: Bonn.
Zandkarimi, F., Rennemeier, J. and Rehse, J.-R. (2021). Are we doing things right? An approach to measure process inefficiencies in the control flow. In , Business Process Management Forum : BPM Forum 2021, Rome, Italy, September 06–10, 2021, Proceedings (S. 109–125). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u.a.].
Berrang, A., Houy, C., Rehse, J.-R. and Fettke, P. (2020). Prozessorientierte Schulung von Einsatzkräften für robotergestützte Rettungsmissionen der Feuerwehr. In , Entwicklungen, Chancen und Herausforderungen der Digitalisierung : Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik, WI 2020, Potsdam, Germany, March 9–11, 2020 (S. 153–167). , GITO Verlag für Industrielle Informationstechnik und Organisation: Berlin.
Hussung, C., Rehse, J.-R., Houy, C. and 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, WI 2020, Potsdam, Germany, March 9–11, 2020 (S. 522–537). , GITO Verlag für Industrielle Informationstechnik und Organisation: Berlin.
Rebmann, A., Rehse, J.-R., Pinter, M., Schnaubelt, M., Daun, K. and 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.
Rehse, J. (2020). Leveraging artificial intelligence for business process management (extended abstract) : A contribution to reference model mining, predictive process monitoring, and process discovery. In , BPM-D 2020 : Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Track at BPM 2020 co-located with the 18th International Conference on Business Process Management (BPM 2020) Sevilla, Spain, September 13–18, 2020 (S. 11–15). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Rehse, J.-R., Striewe, M. and Ullrich, M. (2020). 2. Workshop zur Modellierung in der Hochschullehre. In , MODELLIERUNG-C 2020 : Companion proceedings of Modellierung 2020 Short, Workshop and Tools & Demo Papers, co-located with Modellierung 2020, Vienna, Austria, February 19–21, 2020 (S. 56–57). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Schuhmann, S., Rehse, J.-R., Baumann, S. and Fettke, P. (2020). Interactive process clustering with t-SNE. In , BPM-D 2020 : Proceedings of the Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Track at BPM 2020 co-located with the 18th International Conference on Business Process Management (BPM 2020) Sevilla, Spain, September 13–18, 2020 (S. 82–86). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
Zandkarimi, F., Rehse, J.-R., Soudmand, P. and 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). 2020 2nd International Conference on Process Mining (ICPM), IEEE Computer Society: Los Alamitos, CA [u.a.].
Hake, P., Rehse, J.-R. and 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 : LNBIP, Springer: Berlin [u.a.].
Houy, C., Rehse, J.-R., Scheid, M. and 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). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u.a.].
Rehse, J.-R. and 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 : LNBIP, Springer: Berlin [u.a.].
Willms, C., Houy, C., Rehse, J.-R., Fettke, P. and 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: Aachen, Germany.
Rehse, J.-R. and 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 : LNBIP, Springer: Berlin [u.a.].
Rehse, J.-R., Fettke, P. and 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 : LNBIP, Springer: Berlin [u.a.].
Tenschert, J., Rehse, J.-R., Fettke, P. and 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 : LNBIP, Springer: Berlin [u.a.].
Evermann, J., Rehse, J.-R. and 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: Aachen, Germany.
Rehse, J.-R. and 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. and 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 : LNBIP, Springer: Berlin [u.a.].
Evermann, J., Rehse, J.-R. and 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. and 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. 11–22). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u.a.].
Rehse, J.-R., Fettke, P. and 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). Lecture Notes in Business Information Processing : LNBIP, Springer: Berlin [u.a.].
Rehse, J.-R., Hake, P., Fettke, P. and 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, Ges. für Informatik: Bonn.

Gutermuth, O., Lahann, J., Rehse, J.-R., Scheid, M., Schuhmann, S., Stephan, S. and 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.

Rehse, J. (2019). Leveraging artificial intelligence for business process management : A contribution to reference model mining, predictive process monitoring and process discovery. Dissertation. Saarbrücken.

Scheid, M., Rehse, J.-R., Houy, C. and Fettke, P. (2018). Data set for MobIS Challenge 2019.

Find the Publications in Google Scholar