Auflistung nach Autor:in "Rehse, Jana-Rebecca"
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- Textdokument7. Workshop zum Stand und den Herausforderungen des Geschäftsprozessmanagements(INFORMATIK 2021, 2021) Laue, Ralf; Rehse, Jana-Rebecca; Schoormann, Thorsten
- ZeitschriftenartikelBusiness process management for Industry 4.0 – Three application cases in the DFKI-Smart-Lego-Factory(it - Information Technology: Vol. 60, No. 3, 2018) Rehse, Jana-Rebecca; Dadashnia, Sharam; Fettke, PeterThe advent of Industry 4.0 is expected to dramatically change the manufacturing industry as we know it today. Highly standardized, rigid manufacturing processes need to become self-organizing and decentralized. This flexibility leads to new challenges to the management of smart factories in general and production planning and control in particular. In this contribution, we illustrate how established techniques from Business Process Management (BPM) hold great potential to conquer challenges in Industry 4.0. Therefore, we show three application cases based on the DFKI-Smart-Lego-Factory, a fully automated “smart factory” built out of LEGO ® bricks, which demonstrates the potentials of BPM methodology for Industry 4.0 in an innovative, yet easily accessible way. For each application case (model-based management, process mining, prediction of manufacturing processes) in a smart factory, we describe the specific challenges of Industry 4.0, how BPM can be used to address these challenges, and, their realization within the DFKI-Smart-Lego-Factory.
- KonferenzbeitragInductive reference model development: recent results and current challenges(Informatik 2016, 2016) Rehse, Jana-Rebecca; Hake, Philip; Fettke, Peter; Loos, Peter
- ZeitschriftenartikelThe MobIS-Challenge 2019(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 5, 2020) Baier, Stephan; Dunzer, Sebastian; Fettke,; Houy, Constantin; Matzner, Martin; Pfeiffer, Peter; Rehse, Jana-Rebecca; Scheid, Martin; Stephan, Sebastian; Stierle, Matthias; Willems, BrianInformation systems (IS) can significantly support the organization of business processes. However, the proceeding digitalization of processes can also lead to an increasing organizational complexity and the need to more intensely investigate the adherence to external or internal compliance rules. Process-related data from IS and underlying process models can, however, also contribute to an effective compliance checking. This paper summarizes the motivation, the setup, the data set and the results of the 2019 MobIS-Challenge which was conducted as a workshop at WI 2019 in Siegen, Germany. Results submitted to the challenge are presented in detail and directions for future work are discussed.
- ZeitschriftenartikelA Procedure Model for Situational Reference Model Mining(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 14, Nr. 3, 2019) Rehse, Jana-Rebecca; Fettke, PeterThis contribution introduces the concept of Situational Reference Model Mining, i. e., the idea that automatically derived reference models, although derived from identical input data, are intended for different purposes and therefore have to meet different requirements. These requirements determine the reference model character and thus the technique that is best suited for mining it. Situational Reference Model Mining is based on well-known design principles for reference modeling, such as configuration, aggregation, specialization, instantiation, and analogy. We present a procedure model for Situational Reference Model Mining and demonstrate its usefulness by means of a case study. Existing techniques for Reference Model Mining are examined and mapped to their underlying design principles. Our approach provides reference model designers with first guidelines regarding their choice of mining technique and points out research gaps for the development of new approaches to reference model mining.
- TextdokumentProcess Mining Meets Visual Analytics(EMISA 2024, 2024) Pufahl, Luise; Grohs, Michael; Klein, Lisa-Marie; Rehse, Jana-RebeccaThis extended abstract summarizes a study on the visualization of conformance checking results in process mining, presented at HICSS 2023. Conformance checking compares intended and actual business process behaviors using IT system logs. Our study examined the visualization features of both academic and commercial process mining tools. We found these tools offer visualizations for quantifying conformance, breaking it down, localizing, and explaining deviations. However, there is a need for structured research on process analysts' visualization needs and the interaction between data, analysts, and visualizations.
- TextdokumentStand, Herausforderungen und Impulse des Geschäftsprozessmanagements(INFORMATIK 2020, 2021) Fellmann, Michael; Laue, Ralf; Lantow, Birger; Rehse, Jana-Rebecca
- TextdokumentTowards an Automated Assessment of Graphical (Business Process) Modelling Competences(INFORMATIK 2020, 2021) Striewe, Michael; Houy, Constantin; Rehse, Jana-Rebecca; Ullrich, Meike; Fettke, Peter; Schaper, Niclas; Oberweis, AndreasIn Business Process Management (BPM), graphical process modelling plays an important role because process models can significantly support BPM endeavors in many different ways. Hence, it is also crucial in the context of learning and teaching BPM. Graphical modelling in general is also a curricular core component of higher education in related disciplines such as information systems engineering or software engineering. There are numerous concepts and tools which support learning and teaching of graphical modelling, but most of them have so far been isolated from each other and are used only locally. In order to increase the quality of learning and teaching in modelling courses, it is desirable to better integrate these approaches and identify commonalities. This paper discusses several challenges of integrating learning and teaching approaches for graphical modelling and outlines an approach to a solution that is currently pursued in the ongoing research project KEA-Mod.
- ZeitschriftenartikelTowards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory(KI - Künstliche Intelligenz: Vol. 33, No. 2, 2019) Rehse, Jana-Rebecca; Mehdiyev, Nijat; Fettke, PeterWith the advent of digitization on the shopfloor and the developments of Industry 4.0, companies are faced with opportunities and challenges alike. This can be illustrated by the example of AI-based process predictions, which can be valuable for real-time process management in a smart factory. However, to constructively collaborate with such a prediction, users need to establish confidence in its decisions. Explainable artificial intelligence (XAI) has emerged as a new research area to enable humans to understand, trust, and manage the AI they work with. In this contribution, we illustrate the opportunities and challenges of process predictions and XAI for Industry 4.0 with the DFKI-Smart-Lego-Factory. This fully automated factory prototype built out of LEGO $$^\circledR$$ ® bricks demonstrates the potentials of Industry 4.0 in an innovative, yet easily accessible way. It includes a showcase that predicts likely process outcomes and uses state-of-the-art XAI techniques to explain them to its workers and visitors.