Marius Breitmayer

Marius studierte an der Universität Ulm Wirtschaftswissenschaften mit Schwerpunkt Informatik und schloss seinen Master im Jahr 2018 erfolgreich ab. In seiner Masterarbeit beschäftigte er sich mit Anwendungsmöglichkeiten von Process Mining Algorithmen zur Analyse von prozessorientierten Fragebögen.

Seine Freizeit verbringt er gerne mit Freunden oder beim Sport.

Forschungsinteressen

  • Process Analysis
  • Process Mining
  • Object-centric Process Management

Publikationen

| 2022 | 2021 | 2020 |

2022

Arnold, Lisa and Breitmayer, Marius and Reichert, Manfred (2022) Progress Determination of a BPM Tool with Ad-Hoc Changes: An Empirical Study. In: 16th International Conference on Research Challenges in Information Science (RCIS 22), Barcelona, Spain, 17-20 May 2022, LNCS, Springer, pp. 107-123. file
Breitmayer, Marius and Arnold, Lisa and Reichert, Manfred (2022) Towards Retrograde Process Analysis in Running Legacy Applications. In: 14th Central European Workshop on Services and their Composition (ZEUS 2022), Hannover, 24-25 February 2022, CEUR Workshop Proceedings 3113. file
Breitmayer, Marius and Arnold, Lisa and Reichert, Manfred (2022) Enabling Conformance Checking for Object Lifecycle Processes. In: 16th International Conference on Research Challenges in Information Science (RCIS 22), Barcelona, Spain, 17-20 May 2022, LNBIP, Springer, pp. 124-131. file
Breitmayer, Marius and Arnold, Lisa and La Rocca, Stephan and Reichert, Manfred (2022) Deriving Event Logs from Legacy Software Systems. In: Workshop at 4th International Conference on Process Mining (ICPM 2022), Bozen - Bolzano, ICPM 2022 Workshops, Springer. file
Breitmayer, Marius and Arnold, Lisa and Reichert, Manfred (2022) Data-Driven Evolution of Activity Forms in Object- and Process-Aware Information Systems. In: 27th International Conference on Cooperative Information Systems (CoopIS 2022), Bozen - Bolzano, 4 -7 October 2022, LNCS 13591, Springer, pp. 186-204. file
Rieder, Mathias and Breitmayer, Marius (2022) Object detection in picking: Handling variety of a warehouse’s articles. In: Hamburg International Conference of Logistics (HICL), Hamburg, November 2022, Hamburg International Conference of Logistics (HICL) 33: 67-90 (2022), epubli. file

2021

Arnold, Lisa and Breitmayer, Marius and Reichert, Manfred (2021) Towards Real-Time Progress Determination of Object-Aware Business Processes. In: 13th Central European Workshop on Services and their Composition, Bamberg. file
Arnold, Lisa and Breitmayer, Marius and Reichert, Manfred (2021) A One-Dimensional Kalman Filter for Real-Time Progress Prediction in Object Lifecycle Processes. In: IEEE Int'l Workshop on Intelligent Digital Architecture, Methods, and Services for Industry 4.0 and Society 5.0 (IDAMS 2021), Workshop at the EDOC 2021 Conference, Gold Coast, Australia, 25 - 26 October 2021, IEEE Computer Society Press. (Accepted for Publication) file
Breitmayer, Marius and Arnold, Lisa and Reichert, Manfred (2021) A Dashboard-based Approach for Monitoring Object-Aware Processes. In: 13th Central European Workshop on Services and their Composition, Bamberg. file

2020

Breitmayer, Marius and Reichert, Manfred (2020) Towards the Discovery of Object-Aware Processes. In: 12th Central European Workshop on Services and their Composition (ZEUS 2020), Potsdam, Germany, February 20-21, 2020, CEUR Workshop Proceedings 2575, CEUR-WS.org, pp. 1-4. file
Pryss, Rüdiger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas (2020) Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study. J Med Internet Res, 22(6), JMIR. file

Betreute Abschlussarbeiten

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Kontakt

Portrait von Marius Breitmayer

Marius Breitmayer

Wissenschaft. Mitarbeiter

Büro: Geb. O27 - Raum 552
Sprechzeiten nach Vereinbarung.

marius.breitmayer(at)uni-ulm.de

Telefon: +49 731 50 24 291
Fax: +49 731 50 24 134