On temporal-fuzziness in temporal Fuzzy databases

  • Werasak Kurutach
  • James Franklin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 720)


We propose a framework for a database model unifying both imprecision and time aspects of data. Particularly, we emphasize that the fuzzy meanings of linguistic data can change with time; we call this property temporal-fuzziness. The major problems arising from the lack of the ability to handle this property have been studied, i.e. users and databases misinterpret the meaning of data. Thus, it is essential to treat temporal-fuzziness within an appropriate framework like ours. In our work, we employ concepts based on fuzzy set theory — possibility theory and linguistic variables — for modeling and evaluating uncertain/imprecise data from three domain types, i.e. quantitative, qualitative, and multivalued logic data domains. To model past data, the tuple time stamping method and the discrete time conceptual model are utilized. We introduce three kinds of measures for each domain type to evaluate data. They can provide upper bounded, lower bounded, and approximate answers to queries. Then, the Temporal Fuzzy Data Model and a metadatabase are proposed. The concept of metadatabase allows the time-variant property of fuzzy meanings to be modeled. Since based on the relational model, our model has a uniform and simple structure.

Key Words

fuzzy data temporal data time temporal-fuzziness measures subjectivity objectivity qualitative data quantitative data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Buckles B. P. and Petry F. E., A Fuzzy Representation of Data For Relational Databases, Fuzzy Sets and Syst. 7, 1982, 213–226CrossRefGoogle Scholar
  2. [2]
    Buckles B. P. and Petry F. E., Uncertainty models in information and database systems, J. Inform. Sci. 11, 1985, 213–226Google Scholar
  3. [3]
    Clifford J. and Croker A., The Historical Relational Data Model (HRDM) and Algebra Based on Life Spans, Proc. 3rd Int. Conf. Data Engineering, Feb. 1987, 528–537Google Scholar
  4. [4]
    Dubois D. and Prade H., Processing Fuzzy Temporal Knowledge, IEEE Trans. Systems, Man, and Cybernetics, Vol. 19, No. 4, July/August 1989, 729–744Google Scholar
  5. [5]
    Dutta S., Generalized Events in Temporal Databases, Proc. 5th Int. Conf. on Data Engineering, Los Angeles, CA, Feb 1989, 118–126Google Scholar
  6. [6]
    Dyreson C. E. and Snodgrass R. T., Historical Indeterminacy, Technical Report TR-91-30a, Dep. of Computer Science, Univ. of Arizona, April 1992Google Scholar
  7. [7]
    Gadia S. K. and Yeung C-S, A Generalized Model for A Relational Temporal Database, Proc. ACM SIGMOD Int. Conf. Management of Data, June 1988, 251–259Google Scholar
  8. [8]
    Kouramajian V. and Elmasri R., A Generalized Temporal Model, Technical Report, Univ. of Texas at Arlington, 1992Google Scholar
  9. [9]
    Prade H. and Testemale C., Generalizing Database Relational Algebra for the Treatment of Incomplete or Uncertain Information and Vague Queries, Inform. Sci. 34, 1984, 115–143CrossRefGoogle Scholar
  10. [10]
    Shenoi S. and Melton A., An Extended Version of the Fuzzy Relational Database Model, Inform. Sci. 52, 1990, 35–52MathSciNetGoogle Scholar
  11. [11]
    Snodgrass R. and Ahn I., A taxonomy of time in databases, SIGMOD Record 14, 1985, 236–246Google Scholar
  12. [12]
    Snodgrass R., The Temporal Query Language TQuel, ACM Trans. on Database Systems, Vol. 12, No. 2, June 1987, 247–298CrossRefGoogle Scholar
  13. [13]
    Vitek M., Fuzzy information and fuzzy time, Proc. IFAC Symp. Fuzzy Information, Knowledge Representation and Decision Analysis, Marseille, France, 1983, 159–162Google Scholar
  14. [14]
    Zadeh L. A., The Concept of a Linguistic Variable and its Application to Approximate Reasoning, Infor. Sci. 8, 1975, 199–248 (I) and 301–357 (II)CrossRefGoogle Scholar
  15. [15]
    Zadeh L. A., Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Syst., Vol. 1, No.1, 3–28Google Scholar
  16. [16]
    Zadeh L. A., PRUF-a meaning representation language for natural language, Int. J. Man-Machine Studies 10, 1978, 395–460Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Werasak Kurutach
    • 1
  • James Franklin
    • 2
  1. 1.School of Computer Science and EngineeringAustralia
  2. 2.School of MathematicsThe University of New South WalesAustralia

Personalised recommendations