International Conference on Conceptual Modeling

Conceptual Modeling pp 287-301 | Cite as

A Conceptual Framework for Large-scale Ecosystem Interoperability

  • Matt Selway
  • Markus Stumptner
  • Wolfgang Mayer
  • Andreas Jordan
  • Georg Grossmann
  • Michael Schrefl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9381)


One of the most significant challenges in information system design is the constant and increasing need to establish interoperability between heterogeneous software systems at increasing scale. The automated translation of data between the data models and languages used by information ecosystems built around official or de facto standards is best addressed using model-driven engineering techniques, but requires handling both data and multiple levels of metadata within a single model. Standard modelling approaches are generally not built for this, compromising modelling outcomes. We establish the SLICER conceptual framework built on multilevel modelling principles and the differentiation of basic semantic relations that dynamically structure the model and can capture existing multilevel notions. Moreover, it provides a natural propagation of constraints over multiple levels of instantiation.


Metamodelling Conceptual models Multilevel modelling 


  1. 1.
    Young, N., Jones, S.: SmartMarket Report: Interoperability in Construction Industry. McGraw Hill, Technical report (2007)Google Scholar
  2. 2.
    Fiatech. Advancing Interoperability for the Capital Projects Industry: A Vision Paper. Technical report, Fiatech, February 2012Google Scholar
  3. 3.
    ISO. ISO 15926 - Part 2: Data Model (2003)Google Scholar
  4. 4.
    MIMOSA. Open Systems Architecture for Enterprise Application Integration (2014)Google Scholar
  5. 5.
    Neumayr, B., Schrefl, M., Thalheim, B.: Modeling techniques for multi-level abstraction. In: Kaschek, R., Delcambre, L. (eds.) The Evolution of Conceptual Modeling. LNCS, vol. 6520, pp. 68–92. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  6. 6.
    Bergamaschi, S., Beneventano, D., Guerra, F., Orsini, M.: Data integration. In: Embley, S.D.W., Thalheim, B. (eds.) Handbook of Conceptual Modeling. Theory, Practice, and Research Challenges, pp. 441–476. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  7. 7.
    Atkinson, C., Kühne, T.: The essence of multilevel metamodeling. In: Gogolla, M., Kobryn, C. (eds.) UML 2001. LNCS, vol. 2185, pp. 19–33. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  8. 8.
    Gonzalez-Perez, C., Henderson-Sellers, B.: A powertype-based metamodelling framework. Softw. Syst. Model. 5(1), 72–90 (2006)CrossRefGoogle Scholar
  9. 9.
    Neumayr, B., Jeusfeld, M.A., Schrefl, M., Schütz, C.: Dual deep instantiation and its ConceptBase implementation. In: Proceeding CAISE 2014, pp. 503–517 (2014)Google Scholar
  10. 10.
    Odell, J.J.: Power types. JOOP 7, 8–12 (1994)Google Scholar
  11. 11.
    Neumayr, B., Grün, K., Schrefl, M.: Multi-level domain modeling with M-objects and M-relationships. In: Proceedings APCCM 2009, pp. 107–116 (2009)Google Scholar
  12. 12.
    Jordan, A., Selway, M., Mayer, W., Grossmann, G., Stumptner, M.: An ontological core for conformance checking in the engineering life-cycle. In: Proceeding Formal Ontology in Information Systems (FOIS 2014). IOS Press (2014)Google Scholar
  13. 13.
    Borgo, S., Franssen, M., Garbacz, P., Kitamura, Y., Mizoguchi, R., Vermaas, P.E.: Technical artifact: an integrated perspective. In: FOMI 2011. IOS Press (2011)Google Scholar
  14. 14.
    Schrefl, M., Stumptner, M.: Behavior consistent specialization of object life cycles. ACM TOSEM 11(1), 92–148 (2002)CrossRefGoogle Scholar
  15. 15.
    Klas, W., Schrefl, M. (eds.): Metaclasses and Their Application. LNCS, vol. 943. Springer, Heidelberg (1995) Google Scholar
  16. 16.
    Goldstein, R.C., Storey, V.C.: Materialization. IEEE Trans. Knowl. Data Eng. 6(5), 835–842 (1994)CrossRefGoogle Scholar
  17. 17.
    Kühne, T.: Contrasting classification with generalisation. In: Proceedings APCCM 2009, pp. 71–78, Australia (2009)Google Scholar
  18. 18.
    de Lara, J., Guerra, E., Cobos, R., Moreno-Llorena, J.: Extending deep meta-modelling for practical model-driven engineering. Comput. 57(1), 36–58 (2014)CrossRefGoogle Scholar
  19. 19.
    Neumayr, B., Schrefl, M.: Abstract vs concrete clabjects in dual deep instantiation. In: Proceeding MULTI 2014 Workshop, pp. 3–12 (2014)Google Scholar
  20. 20.
    Eriksson, O., Henderson-Sellers, B., Ágerfalk, P.J.: Ontological and linguistic metamodelling revisited: a language use approach. Inf. Softw. Technol. 55(12), 2099–2124 (2013)CrossRefGoogle Scholar
  21. 21.
    Smith, B.: Against idiosyncrasy in ontology development. In: Proceeding Formal Ontology in Information Systems (FOIS 2006), pp. 15–26 (2006)Google Scholar
  22. 22.
    Welty, C.A., Guarino, N.: Supporting ontological analysis of taxonomic relationships. Data Knowl. Eng. 39(1), 51–74 (2001)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Matt Selway
    • 1
  • Markus Stumptner
    • 1
  • Wolfgang Mayer
    • 1
  • Andreas Jordan
    • 1
  • Georg Grossmann
    • 1
  • Michael Schrefl
    • 1
  1. 1.Advanced Computing Research CentreUniversity of South AustraliaAdelaideAustralia

Personalised recommendations