Advertisement

A Methodology to Improve Object Oriented Database Systems with Fuzzy Types

  • Nicolás Marín
  • Olga Pons
  • Ignacio J. Blanco
  • María Amparo Vila
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 89)

Abstract

Fuzzy Types are a desirable feature that an Object Oriented Database System (OODB) must have in order to deal with vague structures. This kind of types are defined by means of different levels of precision or amplitude where properties are ordered according to their relationship with the concept represented by the type. The implementation of this new tool can be made building a new layer on an existing OODB, avoiding the development of a whole system which incorporates fuzzy types as an intrinsic characteristic. In this paper, we explain how the typical classes of an OODB can be used in order to represent a fuzzy type. New mechanisms of instantiation and inheritance are also modeled using this approach.

Keywords

Precision Level Object Orient Object Oriented Model Fuzzy Object Behavior Component 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bordogna G., Lucarella D., Pasi G. (1994) A fuzzy object oriented data model. In: Proceedings of FUZZ-IEEE, 313–317.Google Scholar
  2. 2.
    Bordogna G., Pasi G. (1998) Management of linguistic qualification of uncertainty in fuzzy databases. In: Proceedings of Information Processing and Management of Uncertainty in Knowledge Based Systems.Google Scholar
  3. 3.
    Bordogna G., Pasi G. (1999) Typicality based on soft aggregations in fuzzy object oriented databases. In: Proceedings of EUSFLAT, 489–492.Google Scholar
  4. 4.
    Bordogna G., Pasi G., Lucarella D. (1999) A fuzzy object oriented data model for managing vague and uncertain information. International Journal of Intelligent Systems 14: 623–651.CrossRefGoogle Scholar
  5. 5.
    Buneman P., Davidson S., Suciu D. (1995) Programming constructs for unstructured data. In: Proceedings of DBPL.Google Scholar
  6. 6.
    Buneman P., Davidson S., Fernandez M., Suciu D. (1997) Adding structure to unstructured data. In: Proceedings of ICDT, 336–350.Google Scholar
  7. 7.
    George R., Buckles B., Petry F. (1993) Modelling class hierarchies in the fuzzy object-oriented data model. Fuzzy Sets and Systems 60: 259–272.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gyseghem N. V., Caluwe R. D. (1994) Fuzzy object-oriented databases: Some behavioral issues. In: Proceedings of EUFIT, 361–364.Google Scholar
  9. 9.
    Gyseghem N. V., Caluwe R. D. (1998) Imprecision and uncertainty in the UFO database model. Journal of the American Society for Information Science 49: 236252.Google Scholar
  10. 10.
    Marin N., Vila M.A., Pons O. (2000) Fuzzy types: A new concept of type for managing vague structures. International Journal of Intelligent Systems 15: 10611085.Google Scholar
  11. 11.
    Marin N., Blanco I.J., Pons 0., Vila M.A. (2000) Extracting fuzzy types from datasets analyzing null values in attributes. Tech. Rep. DECSAI-00–01-Google Scholar
  12. 21.
    Department of Computer Science and Artificial Intelligence, Universitiy of Granada.Google Scholar
  13. 12.
    Marin N., Blanco I.J., Pons 0., Vila M.A. (2000) Fuzzy types as a new layer on an object oriented database system. In: Proceedings of IPMU, 1099.Google Scholar
  14. 13.
    Marin N., Pons 0., Vila M.A. (2000) Softening the object-oriented database model: imprecision, uncertainty, and fuzzy types. Tech. Rep. DECSAI-00–01-Google Scholar
  15. 22.
    Department of Computer Science and Artificial Intelligence, Universitiy of Granada.Google Scholar
  16. 14.
    Nestorov S., Abiteboul S., Motwani R. (1997) Inferring structure in semistructured data. In: Proceedings of Workshop on Management of Semistructured Data held in conjunction with SIGMOD.Google Scholar
  17. 15.
    Papakonstantinou Y., Garca-Molina H., Widom J. (1995) Object exchange across heterogeneous information sources. In: Proceedings of IEEE International Conference on Data Engineering.Google Scholar
  18. 16.
    Rossazza J.-P., Dubois D., Prade H. (1998) Fuzzy and Uncertain Object-Oriented Databases. Concepts and Models. A hierarchical model of fuzzy classes. 13:21–61. Advances in Fuzzy Systems- Applications and Theory.Google Scholar
  19. 17.
    Vila M.A., Cubero J.C., Medina J.M., Pons O. (1996) A conceptual approach for dealing with imprecision and uncertainty in object-based data models. International Journal of Intelligent Systems 11: 791–806.MATHCrossRefGoogle Scholar
  20. 18.
    Vila M.A., Cubero J.C., Medina J.M., Pons O. (1998) A fuzzy object–oriented data model represented by means of a semantic data model. Tech. Rep. DECSAI98–00–00. Department of Computer Science and Artificial Intelligence – University of Granada.Google Scholar
  21. 19.
    Yazici A., Aksoy D., George R. (1996) The similarity-based fuzzy object-oriented data model. In: Proceedings of IPMU, vol. 3, 1177–1182.Google Scholar
  22. 20.
    Yazici A., Koyuncu M. (1997) Fuzzy object-oriented database modeling coupled with fuzzy logic. Fuzzy Sets and Systems 89: 1–26.CrossRefGoogle Scholar
  23. 21.
    Yazici A., George R., Aksoy D. (1998) Design and implementation issues in the fuzzy object-oriented data model. Journal of Information Sciences 108: 241–260.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Nicolás Marín
    • 1
  • Olga Pons
    • 1
  • Ignacio J. Blanco
    • 1
  • María Amparo Vila
    • 1
  1. 1.Department of Computer Science and Artificial Intelligence E.T.S.I.IUniversity of GranadaGranada, AndalusiaSpain

Personalised recommendations