Advertisement

Querying Multi-granular Compact Representations

  • Romāns Kasperovičs
  • Michael Böhlen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)

Abstract

A common phenomenon of time-qualified data are temporal repetitions, i.e., the association of multiple time values with the same data. In order to deal with finite and infinite temporal repetitions in databases we must use compact representations. There have been many compact representations proposed, however, not all of them are equally efficient for query evaluation. In order to show it, we define a class of simple queries on compact representations. We compare a query evaluation time on our proposed multi-granular compact representation GSequences with a query evaluation time on single-granular compact representation PSets, based on periodical sets. We show experimentally how the performance of query evaluation can benefit from the compactness of a representation and from a special structure of GSequences.

Keywords

Compact Representation Query Evaluation Time Granularity Simple Query Input Tuples 
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.
    Bettini, C., Mascetti, S.: An efficient algorithm for minimizing time granularity periodical representations. In: TIME, pp. 20–25 (2005)Google Scholar
  2. 2.
    Bettini, C., Sibi, R.D.: Symbolic representation of user-defined time granularities. In: Proceedings of TIME 1999, pp. 17–28. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  3. 3.
    Chandra, R., Segev, A., Stonebraker, M.: Implementing calendars and temporal rules in next generation databases. In: Proceedings of the Tenth International Conference on Data Engineering, Washington, DC, USA, pp. 264–273. IEEE Computer Society Press, Los Alamitos (1994)Google Scholar
  4. 4.
    Cukierman, D.R., Delgrande, J.P.: The sol theory: A formalization of structured temporal objects and repetition. In: Proceedings of TIME 2004, IEEE Computer Society, Los Alamitos (2004)Google Scholar
  5. 5.
    Egidi, L., Terenziani, P.: A mathematical framework for the semantics of symbolic languages representing periodic time. In: Proceedings of TIME 2004, IEEE Computer Society, Los Alamitos (2004)Google Scholar
  6. 6.
    Kabanza, F., Stevenne, J.-M., Wolper, P.: Handling infinite temporal data. In: PODS, pp. 392–403 (1990)Google Scholar
  7. 7.
    Leban, B., McDonald, D.D., Forster, D.R.: A representation for collections of temporal intervals. In: Proceedings of AAAI 1986, pp. 367–371 (August 1986)Google Scholar
  8. 8.
    Niezette, M., Stevenne, J.-M.: An efficient symbolic representation of periodic time. In: Proceedings of the First International Conference on Information and Knowledge Management (November 1992)Google Scholar
  9. 9.
    Ning, P., Wang, X.S., Jajodia, S.: An algebraic representation of calendars. Ann. Math. Artif. Intell. 36(1-2), 5–38 (2002)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Terenziani, P.: Symbolic user-defined periodicy in temporal relational databases. IEEE TKDE 15(2) ( March/April 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Romāns Kasperovičs
    • 1
  • Michael Böhlen
    • 1
  1. 1.Free University of Bozen - BolzanoBozen, BolzanoItaly

Personalised recommendations