Efficient Engineering Data Exchange in Multi-disciplinary Systems Engineering

  • Stefan Biffl
  • Arndt Lüder
  • Felix Rinker
  • Laura WaltersdorferEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)


In the parallel engineering of industrial production systems, domain experts from several disciplines need to exchange data efficiently to prevent the divergence of local engineering models. However, the data synchronization is hard (a) as it may be unclear what data consumers need and (b) due to the heterogeneity of local engineering artifacts. In this paper, we introduce use cases and a process for efficient Engineering Data Exchange (EDEx) that guides the definition and semantic mapping of data elements for exchange and facilitates the frequent synchronization between domain experts. We identify main elements of an EDEx information system to automate the EDEx process. We evaluate the effectiveness and effort of the EDEx process and concepts in a feasibility case study with requirements and data from real-world use cases at a large production system engineering company. The domain experts found the EDEx process more effective and the EDEx operation more efficient than the traditional point-to-point process, and providing insight for advanced analyses.


Production systems engineering Data exchange Data integration Process design Multi-aspect information system Multidisciplinary engineering 



The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital & Economic Affairs and the National Foundation for Research, Technology and Development is gratefully acknowledged.


  1. 1.
    Biffl, S., Eckhart, M., Lüder, A., Müller, T., Rinker, F., Winkler, D.: Data interface for coil car simulation (case study) part I. Technical report CDL-SQI-M2-TR02, TU Wien (2018)Google Scholar
  2. 2.
    Biffl, S., Eckhart, M., Lüder, A., Müller, T., Rinker, F., Winkler, D.: Data interface for coil car simulation (case study) part II - detailed data and process models. Technical report CDL-SQI-M2-TR03, TU Wien (2018)Google Scholar
  3. 3.
    Biffl, S., Gerhard, D., Lüder, A.: Introduction to the multi-disciplinary engineering for cyber-physical production systems. In: Biffl, S., Lüder, A., Gerhard, D. (eds.) Multi-Disciplinary Engineering for Cyber-Physical Production Systems, pp. 1–24. Springer, Cham (2017). Scholar
  4. 4.
    Biffl, S., Lüder, A., Rinker, F., Waltersdorfer, L., Winkler, D.: Introducing engineering data logistics for production systems engineering. Technical report CDL-SQI-2018-10, TU Wien (2018).
  5. 5.
    Biffl, S., Winkler, D., Mordinyi, R., Scheiber, S., Holl, G.: Efficient monitoring of multi-disciplinary engineering constraints with semantic data integration in the multi-model dashboard process. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA), pp. 1–10. IEEE (2014)Google Scholar
  6. 6.
    Brambilla, M., Cabot, J., Wimmer, M.: Model-driven software engineering in practice. Synth. Lect. Softw. Eng. 1(1), 1–182 (2012)CrossRefGoogle Scholar
  7. 7.
    Calà, A., et al.: Migration from traditional towards cyber-physical production systems. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 1147–152. IEEE (2017)Google Scholar
  8. 8.
    Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: Theory and results. Ph.D. thesis, Massachusetts Institute of Technology (1985)Google Scholar
  9. 9.
    Hohpe, G., Woolf, B.: Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. Addison-Wesley Professional, Boston (2004)Google Scholar
  10. 10.
    Jimenez-Ramirez, A., Barba, I., Reichert, M., Weber, B., Del Valle, C.: Clinical processes - the Killer application for constraint-based process interactions? In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 374–390. Springer, Cham (2018). Scholar
  11. 11.
    Kovalenko, O., Euzenat, J.: Semantic matching of engineering data structures. Semantic Web Technologies for Intelligent Engineering Applications, pp. 137–157. Springer, Cham (2016). Scholar
  12. 12.
    Lüder, A., Pauly, J.-L., Kirchheim, K., Rinker, F., Biffl, S.: Migration to AutomationML based tool chains - incrementally overcoming engineering network challenges. In: 5th AutomationML User Conference (2018)Google Scholar
  13. 13.
    Business Process Model. Notation (BPMN) Version 2.0. omg (2011)Google Scholar
  14. 14.
    Putze, S., Porzel, R., Savino, G.-L., Malaka, R.: A manageable model for experimental research data: an empirical study in the materials sciences. In: Krogstie, J., Reijers, H.A. (eds.) CAiSE 2018. LNCS, vol. 10816, pp. 424–439. Springer, Cham (2018). Scholar
  15. 15.
    Rosemann, M., vom Brocke, J.: The six core elements of business process management. In: vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management 1. IHIS, pp. 105–122. Springer, Heidelberg (2015). Scholar
  16. 16.
    Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empirical Softw. Eng. 14(2), 131 (2009)CrossRefGoogle Scholar
  17. 17.
    Sabou, M., Ekaputra, F.J., Biffl, S.: Semantic web technologies for data integration in multi-disciplinary engineering. In: Biffl, S., Lüder, A., Gerhard, D. (eds.) Multi-disciplinary Engineering for Cyber-Physical Production Systems, pp. 301–329. Springer, Cham (2017). Scholar
  18. 18.
    Vogel-Heuser, B., Bauernhansl, T., Ten Hompel, M.: Handbuch industrie 4.0 bd. 4. Allgemeine Grundlagen, vol. 2 (2017)Google Scholar
  19. 19.
    Wieringa, R.J.: Design Science Methodology for Information Systems and Software Engineering. Springer, Heidelberg (2014). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stefan Biffl
    • 1
  • Arndt Lüder
    • 2
  • Felix Rinker
    • 1
    • 3
  • Laura Waltersdorfer
    • 1
    Email author
  1. 1.Institute of Information Systems EngineeringTechnische Universität WienViennaAustria
  2. 2.Institute of Factory AutomationOtto-von-Guericke University MagdeburgMagdeburgGermany
  3. 3.Christian Doppler Laboratory for Security and Quality Improvement in the Production System Lifecycle (CDL-SQI)Technische Universität WienViennaAustria

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