Advertisement

On Uncertainty and Data-Warehouse Design

  • Panagiotis Chountas
  • Ilias Petrounias
  • Christos Vasilakis
  • Andy Tseng
  • Elia El-Darzi
  • Krassimir T. Atanassov
  • Vassilis Kodogiannis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3261)

Abstract

In this paper we informally and formally defined what we mean by uncertain- ignorant information in relational databases and data warehouses. We classify proposed extensions to the relational data model that can represent and retrieve incomplete information. There are many different kinds of temporal ignorant information including information that is fuzzy, imprecise, indeterminate, indefinite, missing, partial, possible, probabilistic, unknown, uncertain, or vague. We will explore each variety of temporal ignorant information in detail with reference to database and data-warehouse design.

Keywords

Data Warehouse Case Base Reasoning Versus Versus Versus Versus Versus Fact Table Temporal Uncertainty 
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.
    Anahory, S., Murray, D.: Data warehousing in the real world: A Practical Approach for Building Decision Support Systems. Addison-Wesley, Reading (1997)Google Scholar
  2. 2.
    Böhlen, M., et al.: Point-versus Interval-Based Temporal Data Models. In: Proceedings of 14th ICDE, pp. 192–201. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  3. 3.
    Chountas, P., Petrounias, I., Atanassov, K., Kodogiannis, V., El-Darzi, E.: In: Andreasen, T., Motro, A., Christiansen, H., Larsen, H.L. (eds.) FQAS 2002. LNCS (LNAI), vol. 2522, pp. 112–123. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Etzion, O., Jajodia, S., Sripada, S.: In: Etzion, O., Jajodia, S., Sripada, S. (eds.) Dagstuhl Seminar 1997. LNCS, vol. 1399, Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Inmon, W.: Building the Data Warehouse, 2nd edn. John Wiley & Sons, New York (1996)Google Scholar
  6. 6.
    Jensen, C., Snodgrass, R.: Temporal Data Management. IEEE Transactions on Knowledge and Data Engineering 11(1), 36–44 (1999)CrossRefGoogle Scholar
  7. 7.
    Kimball, R.: The Data Warehouse Toolkit. John Wiley & Sons, New York (1996)Google Scholar
  8. 8.
    Pedersen, T., Jensen, C.: Multidimensional Data Modeling for Complex Data. In: Proceedings of 15th ICDE, pp. 336–345. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  9. 9.
    Snodgrass, R.: Monitoring Distributed Systems: A Relational Approach, Ph.D. Dissertation. Carnegie Mellon University (1982)Google Scholar
  10. 10.
    Brusoni, V., Console, L., Terenziani, P., Pernici, B.: Extending Temporal Relational Databases to deal with Imprecise and Qualitative Temporal Information. In: Proceedings of the International Workshop on Recent Advances in Temporal Databases, pp. 3–22. Springer, Heidelberg (1995)Google Scholar
  11. 11.
    Dyreson, C., Snodgrass, R.: Supporting Valid-time Indeterminacy. ACM Trans. Database Systems 23(1), 1–57 (1998)CrossRefGoogle Scholar
  12. 12.
    Dekhtyar, R., Ross, V.S.: Probabilistic Temporal Databases, I: Algebra. ACM Trans. on Database Systems 26(1), 41–95 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Koubarakis, M.: Database Models for Infinite and Indefinite Temporal Information. Inf. Systems 19(2), 141–173 (1994)CrossRefGoogle Scholar
  14. 14.
    Koubarakis, M.: Representation and Querying in Temporal databases: The power of temporal constraints. In: Proceedings of the International Conference on Data Engineering, pp. 327–334 (1993)Google Scholar
  15. 15.
    Dubois, D., Prade, H.: Processing Fuzzy Temporal Knowledge. IEEE Trans. Systems Man Cybernetics 19(4), 729–744 (1989)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Dutta, S.: Generalized Events in Temporal Databases. In: Proceedings of the 5th International Conference on Data Engineering, pp. 118–126. IEEE Computer Society, Los Alamitos (1989)CrossRefGoogle Scholar
  17. 17.
    Trevsky, I.G.: Studies of Similarity, Cognition and Categorization, Hillsdale, NJ, Erlbaum (1978)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Panagiotis Chountas
    • 1
  • Ilias Petrounias
    • 2
  • Christos Vasilakis
    • 1
  • Andy Tseng
    • 2
  • Elia El-Darzi
    • 1
  • Krassimir T. Atanassov
    • 3
  • Vassilis Kodogiannis
    • 1
  1. 1.Health Care Computing Group, School of Computer ScienceUniversity of WestminsterLondonUK
  2. 2.Department of ComputationUMISTManchesterUK
  3. 3.CLBMEBulgarian Academy of SciencesSofiaBulgaria

Personalised recommendations