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Abstract

Representing and reasoning over evolving objects has been investigated widely. Less attention has been devoted to the similar notion of relation migration, i.e., how tuples of a relation (ORM facts) can evolve along time. We identify different ways how a relation can change over time and give a logic-based semantics to the notion of relation migration to capture its behaviour. We also introduce the notion of lifespan of a relation and clarify the interactions between object migration and relation migration. Its use in graphical conceptual data modelling is illustrated with a minor extension to ORM2 so as to more easily communicate such constraints with domain experts.

Keywords

Entity Type Temporal Instant Logical Implication Business Rule Relation Migration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Artale, A., Parent, C., Spaccapietra, S.: Evolving objects in temporal information systems. Annals of Mathematics and Artificial Intelligence 50(1-2), 5–38 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Balsters, H., Carver, A., Halpin, T., Morgan, T.: Modeling dynamic rules in ORM. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4278, pp. 1201–1210. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Hall, G., Gupta, R.: Modeling transition. In: Proc. of ICDE 1991, pp. 540–549 (1991)Google Scholar
  4. 4.
    Etzion, O., Gal, A., Segev, A.: Extended update functionality in temporal databases. In: Etzion, O., Jajodia, S., Sripada, S. (eds.) Dagstuhl Seminar 1997. LNCS, vol. 1399, pp. 56–95. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Halpin, T.: Temporal modeling and ORM. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2008. LNCS, vol. 5333, pp. 688–698. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Parent, C., Spaccapietra, S., Zimányi, E.: Conceptual modeling for traditional and spatio-temporal applications—the MADS approach. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  7. 7.
    Artale, A., Guarino, N., Keet, C.M.: Formalising temporal constraints on part-whole relations. In: Brewka, G., Lang, J. (eds.) 11th International Conference on Principles of Knowledge Representation and Reasoning (KR 2008), pp. 673–683. AAAI Press, Menlo Park (2008)Google Scholar
  8. 8.
    Artale, A., Keet, C.M.: Essential, mandatory, and shared parts in conceptual data models. In: Halpin, T.A., Proper, H.A., Krogstie, J. (eds.) Innovations in Information Systems modeling: Methods and Best Practices, pp. 17–52. IGI Global (2008)Google Scholar
  9. 9.
    Keet, C.M., Artale, A.: Representing and reasoning over a taxonomy of part-whole relations. Applied Ontology 3(1-2), 91–110 (2008)Google Scholar
  10. 10.
    Guizzardi, G.: Ontological Foundations for Structural Conceptual Models. Phd thesis, University of Twente, The Netherlands. Telematica Instituut Fundamental Research Series No. 15 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • C. Maria Keet
    • 1
  • Alessandro Artale
    • 1
  1. 1.KRDB Research CentreFree University of Bozen-BolzanoItaly

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