Domain Specific Models as System Links

  • Vladimir A. Shekhovtsov
  • Suneth Ranasinghe
  • Heinrich C. MayrEmail author
  • Judith Michael
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11158)


Digital Ecosystems consist of a variety of interlinked subsystems. This paper presents a flexible approach to define the links between such subsystems. The idea is to exploit the paradigm of Model Centered Architecture (MCA) and to specify all links/interfaces by means of appropriate Domain Specific Modeling Languages. The approach has been successfully applied and evaluated in several projects. As a proof of concept, we present the model-based interfacing between assistive systems and human activity recognition systems, which showed good performance as needed in real-world applications.


Model Centered Architecture Domain Specific Modeling Digital ecosystem Language hierarchies Interfacing 


  1. 1.
    Bencomo, N., Bennaceur, A., Grace, P., Blair, G., Issarny, V.: The role of models@ run. time in supporting on-the-fly interoperability. Computing 95, 167–190 (2013)CrossRefGoogle Scholar
  2. 2.
    Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U. (eds.): Models@run.time: Foundations, Applications, and Roadmaps. LNCS, vol. 8378. Springer, Cham (2014). Scholar
  3. 3.
    Bennaceur, A., Issarny, V.: Automated synthesis of mediators to support component interoperability. IEEE Trans. Softw. Eng. 41, 221–240 (2015)CrossRefGoogle Scholar
  4. 4.
    Chen, L., Khalil, I.: Activity recognition: approaches, practices and trends. In: Chen, L., Nugent, C., Biswas, J., Hoey, J. (eds.) Activity Recognition in Pervasive Intelligent Environments, pp. 1–31. Springer, Paris (2011). Scholar
  5. 5.
    Costa, F.M., Morris, K.A., Kon, F., Clarke, P.J.: Model-driven domain-specific middleware. In: Proceedings of ICDCS 2017, pp. 1961–1971. IEEE (2017)Google Scholar
  6. 6.
    Embley, D.W., Liddle, S.W., Pastor, O.: Conceptual-model programming: a manifesto. In: Embley, D., Thalheim, B. (eds.) Handbook of Conceptual Modeling, pp. 3–16. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Fill, H.-G., Karagiannis, D.: On the conceptualisation of modelling methods using the ADOxx meta modelling platform. EMISA J. 8, 4–25 (2013)Google Scholar
  8. 8.
    Götz, S., et al.: Adaptive exchange of distributed partial models@run.time for highly dynamic systems. In: Proceedings of SEAMS 2015, pp. 64–70. IEEE Press (2015)Google Scholar
  9. 9.
    Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137 (2015)Google Scholar
  10. 10.
    Kofod-Petersen, A., Cassens, J.: Using activity theory to model context awareness. In: Roth-Berghofer, T.R., Schulz, S., Leake, D.B. (eds.) MRC 2005. LNCS (LNAI), vol. 3946, pp. 1–17. Springer, Heidelberg (2006). Scholar
  11. 11.
    Liddle, S.W.: Model-driven software development. In: Embley, D.W., Thalheim, B. (eds.) Handbook of Conceptual Modeling, pp. 17–54. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Mayr, H.C., et al.: HCM-L: domain-specific modeling for active and assisted living. In: Karagiannis, D., Mayr, H., Mylopoulos, J. (eds.) Domain-Specific Conceptual Modeling, pp. 527–552. Springer, Cham (2016)CrossRefGoogle Scholar
  13. 13.
    Mayr, H.C., et al.: Model centered architecture. In: Cabot, J., Gómez, C., Pastor, O., Sancho, M., Teniente, E. (eds.) Conceptual Modeling Perspectives, pp. 85–104. Springer, Cham (2017). Scholar
  14. 14.
    Meditskos, G., et al.: MetaQ. Perv. Mob. Comput. 25, 104–124 (2016)CrossRefGoogle Scholar
  15. 15.
    Michael, J., Steinberger, C.: Context Modeling for Active Assistance. In: Proceedings of ER Forum 2017. CEUR Workshop Proceedings 1979,, pp. 207–220 (2017)Google Scholar
  16. 16.
    Michael, J., et al.: The HBMS Story. Enterp. Model. Inf. Syst. Arch. 13, 345–370 (2018)Google Scholar
  17. 17.
    Ni, Q., et al.: A foundational ontology-based model for human activity representation in smart homes. J. Ambient. Intell. Smart Environ. 8, 47–61 (2016)CrossRefGoogle Scholar
  18. 18.
    Onofri, L., et al.: A survey on using domain and contextual knowledge for human activity recognition in video streams. Expert Syst. Appl. 63, 97–111 (2016)CrossRefGoogle Scholar
  19. 19.
    Peláez, M.D., López-Medina, M., Espinilla, M., Medina-Quero, J.: Key factors for innovative developments on health sensor-based system. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2017,Part II. LNCS, vol. 10209, pp. 665–675. Springer, Cham (2017). Scholar
  20. 20.
    Ranasinghe, S., et al.: A review on applications of activity recognition systems with regard to performance and evaluation. Int. J. Distrib. Sens. Netw. 12, 1550147716665520 (2016)CrossRefGoogle Scholar
  21. 21.
    Rodríguez, N.D., Cuéllar, M.P., Lilius, J., Calvo-Flores, M.D.: A survey on ontologies for human behavior recognition (CSUR). ACM Comput. Surv. 46, 43 (2014)CrossRefGoogle Scholar
  22. 22.
    Siegel, C., Dorner, T.E.: Information technologies for active and assisted living—Influences to the quality of life of an ageing society. Int. J. of Med. Inform. 100, 32–45 (2017)CrossRefGoogle Scholar
  23. 23.
    Zgheib, R., et al.: A flexible architecture for cognitive sensing of activities in ambient assisted living. In: Proceedings of WETICE 2017, pp. 284–289. IEEE (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Alpen-Adria-UniversitätKlagenfurtAustria
  2. 2.Software EngineeringRWTH Aachen UniversityAachenGermany

Personalised recommendations