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3rd Workshop on

Domain-Specific Modeling Methods and Tools - OMiLAB Nodes Experience & Knowledge Exchange (OMiLAB-KNOW)

October 9th, 2026
co-located with the
- 25th International Conference on Perspectives in Business Informatics Research (https://www.bir2026.fhnw.ch/) -

Workshop Description

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OMiLAB-KNOW is a recurring workshop series focused on domain-specific conceptual modeling methods and tools in the context of Business Informatics Research. Building on previous editions, it continues the OMiLAB mission of advancing model-driven value creation through openness, interdisciplinarity, as well as community-based collaboration and knowledge exchange. The primary goal of the workshop series is to stimulate discussion on the requirements, design decisions, tooling, utilization, and evaluation of artifacts related to domain-specific conceptual modeling, as showcased by the various areas covered within publications from the network (e.g., [1, 2, 3, 4, 5]). The broader objectives aim to facilitate the realization of model-driven support through open frameworks and interdisciplinary innovation across various domains.

It is expected that workshop contributions report on experiences and achievements from the global OMiLAB Network of Nodes and the extended Community of Practice [6, 7] that utilize the underlying, distributed Digital Innovation Environment [8, 9, 10, 11]. Such experience can also encompass results and lessons learned from recent research and innovation projects (e.g., [12, 13, 14, 15]), as well as domain-specific experimental results, targeting innovative solution design using model-based approaches and beyond.

While the workshop is initiated by the OMiLAB community, it also welcomes participation from researchers and educators not yet involved in the network. In particular, anyone active in relevant fields such as domain-specific modeling, innovation infrastructures and processes, enterprise modeling, knowledge engineering, and interdisciplinary knowledge management, with an interest in the value of model-driven system design, is invited to contribute. This approach opens up the opportunity for exchanging ideas with other modeling-centric communities to account for the diverse views on modeling challenges and best practices. Consistent with ongoing research projects and previous workshop editions, the scope of OMiLAB-KNOW extends to the integration of conceptual modeling into education, professional training, and related teaching experiences [16, 17]. Moreover, recent developments highlight the need for considering Digital Wellbeing in the context of conceptual modeling. This novel field explores how model-driven approaches, tools, and collaborative practices can be designed to enhance cognitive, social, and digital wellbeing for individuals and communities engaged in conceptual modeling [18].

Given these considerations, submission types may span various research stages, from novel findings to practical experience reports and position papers. Therefore, both empirical experimentation insights and design-oriented research are welcome. Specifically, we encourage young researchers to share their findings and work-in-progress results.

Relevant Topics (but not limited to)

  • Domain-specific modeling languages for business informatics
  • Integration of domain-specific modeling with Artificial Intelligence
  • Model-driven approaches for low-code and no-code engineering
  • Requirements engineering, design, and evaluation of conceptual modeling methods
  • Engineering, deployment, and lifecycle management of modeling methods
  • Enterprise and ecosystem modeling, including enterprise architecture
  • Modeling tools, platforms, and extensibility (e.g., plugins, frameworks)
  • Transformations from conceptual models to knowledge graphs, digital twins, and other artifacts
  • Model-driven engineering and value creation through model-based artifacts
  • Applications, use cases, and experience reports demonstrating model value
  • Empirical research on modeling practices, quality, and effectiveness
  • Educational approaches, teaching cases, and training in conceptual modeling
  • Wellbeing-aware conceptual modeling, including cognitive load and digital wellbeing
  • Human-centered and socio-technical perspectives on model-driven systems and collaboration
  • Communities of practice, collaborative modeling, and knowledge exchange ecosystems

References

  1. B. Lantow, K. Sandkuhl und J. Stirna, „Enterprise Modeling with 4EM: Perspectives and Method”, In: Domain-Specific Conceptual Modeling, Springer International Publishing, 2022, p. 95–120. https://doi.org/10.1007/978-3-030-93547-4_5
  2. A. Chis, R. A. Buchmann und A.-M. Ghiran, „Towards a modeling method for low-code knowledge graph building”, In: Proceedings of the 16th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling and the 13th Enterprise Design and Engineering Working Conference (PoEM & EDEWC - Companion 2023), 2023. https://ceur-ws.org/Vol-3645/forum4.pdf
  3. R. Woitsch, C. Muck, W. Utz und H. Zeiner, „Towards a democratic AI-based decision support system to improve decision making in complex ecosystems”, In: Joint Proceedings of the BIR 2023 Workshops and Doctoral Consortium co-located with 22nd International Conference on Perspectives in Business Informatics Research (BIR 2023), 2023.https://ceur-ws.org/Vol-3514/paper94.pdf
  4. C. Muck, J. Tschuden, H. Zeiner und W. Utz, „Explainability of Industrial Decision Support System using Digital Design Thinking with Scene2Model”, In: Cognitive Computing and Internet of Things, 2024.https://doi.org/10.54941/ahfe1004710
  5. W. Utz, A. Völz, C. Muck, H. A. Zhou, R. Woitsch, K. Hinkelmann und R. H. Schmitt, „Opening the Black Box: Explaining Generative AI Interactions using Scene2Model”, In: The AI, Data and Robotics Association, 2026. (Accepted for ADR Open Access Book)
  6. I. Vaidian, A. Jurczuk, Z. Misiak, M. Neidow, M. Petry und M. Nemetz, „Challenging Digital Innovation Through the OMiLAB Community of Practice”, In: Domain-Specific Conceptual Modeling: Concepts, Methods and ADOxx Tools, D. Karagiannis, M. Lee, K. Hinkelmann und W. Utz, Hrsg., Cham, Springer International Publishing, 2022, p. 41–64.https://doi.org/10.1007/978-3-030-93547-4_3
  7. The OMiLAB Community, „Development of Conceptual Models and Realization of Modelling Tools Within the ADOxx Meta-Modelling Environment: A Living Paper”, In Domain-Specific Conceptual Modeling, D. Karagiannis, M. Lee, K. Hinkelmann und W. Utz, Hrsg., Springer International Publishing, 2022, p. 23–40.https://doi.org/10.1007/978-3-030-93547-4_2
  8. OMiLAB Team, A Digital Innovation Environment powered by Open Models Laboratory, 2020.https://doi.org/10.5281/zenodo.3899990
  9. R. Woitsch, „Industrial Digital Environments in Action: The OMiLAB Innovation Corner”, In: The Practice of Enterprise Modeling, J. Grabis und D. Bork, Hrsg., Springer International Publishing, 2020, p. 8–22.https://doi.org/10.1007/978-3-030-63479-7_2
  10. D. Karagiannis, R. A. Buchmann, X. Boucher, S. Cavalieri, A. Florea, D. Kiritsis und M. Lee, „OMiLAB: A Smart Innovation Environment for Digital Engineers”, In: Boosting Collaborative Networks 4.0, L. M. Camarinha-Matos, H. Afsarmanesh und A. Ortiz, Hrsg., Cham, Springer International Publishing, 2020, p. 273–282.https://doi.org/10.1007/978-3-030-62412-5_23
  11. D. Karagiannis, R. A. Buchmann und W. Utz, „The OMiLAB Digital Innovation environment: Agile conceptual models to bridge business value with Digital and Physical Twins for Product-Service Systems development”, Computers in Industry 138, p. 103631, 2022.https://doi.org/10.1016/j.compind.2022.103631
  12. FAIRWork - Flexibilization of complex Ecosystems using Democratic AI based Decision Support and Recommendation Systems at Work.https://fairwork-project.eu/
  13. CoDEMO - Co-Creative Decision-Makers for 5.0 Organizations.https://www.codemo-project.eu/
  14. A. Voelz, I. Vaidian und C. Muck, „Digital Twins for Haptic Design Thinking: Application within CoDEMO 5.0”, In: 18th International Conference on Research Challenges in Information Science (RCIS 2024), 2024.https://ceur-ws.org/Vol-3674/RP-paper2.pdf
  15. R. Woitsch, C. Muck, W. Utz und H. Zeiner, „Enable Flexibilisation in FAIRWork’s Democratic AI-based Decision Support System by Applying Conceptual Models Using ADOxx”, Complex Systems Informatics and Modeling Quarterly 38, p. 27–53, 2024.https://doi.org/10.7250/csimq.2024-38.02
  16. R. Buchmann, A.-M. Ghiran, V. Döller und D. Karagiannis, „Conceptual Modeling Education as a ‘Design Problem’”, Complex Systems Informatics and Modeling Quarterly 21, p. 21–33, 2019.https://doi.org/10.7250/csimq.2019-21.02
  17. A. Völz und I. Vaidian, „Digital Transformation through Conceptual Modeling: The NEMO Summer School Use Case” In: Modellierung 2024, Bonn, Gesellschaft für Informatik e.V., pp. 139–156, 2024.https://doi.org/10.18420/modellierung2024_014
  18. P.-C. Zangogianni, E. Kavakli, G. Georgiou und D. Kaviri, „Integrating Digital Wellbeing into Enterprise Modeling”, In: Companion Proceedings of the 18th IFIP Working Conference on the Practice of Enterprise Modeling: PoEM Forum, 2025.https://ceur-ws.org/Vol-4171/paper_36.pdf