Adaptive Management of Multigranular Spatio-Temporal Object Attributes

  • Elena Camossi
  • Elisa Bertino
  • Giovanna Guerrini
  • Michela Bertolotto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5644)


In applications involving spatio-temporal modelling, granularities of data may have to adapt according to the evolving semantics and significance of data. In this paper we define ST 2_ODMGe, a multigranular spatio-temporal model supporting evolutions, which encompass the dynamic adaptation of attribute granularities, and the deletion of attribute values. Evolutions are specified as Event - Condition - Action rules and are executed at run-time. The event, the condition, and the action may refer to a period of time and a geographical area. The evolution may also be constrained by the attribute values. The ability of dynamically evolving the object attributes results in a more flexible management of multigranular spatio-temporal data but it requires revisiting the notion of object consistency with respect to class definitions and access to multigranular object values. Both issues are formally investigated in the paper.


Ceria Lution Tempo Hacid 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andrienko, G., Malerba, D., May, M., Teisseire, M.: Mining spatio-temporal data. J. of Intelligent Information Systems 27(3), 187–190 (2006)CrossRefGoogle Scholar
  2. 2.
    Arge, L., de Berg, M., Haverkort, H.J., Yi, K.: The Priority R-Tree: A Practically Efficient and Worst-Case Optimal R-Tree. In: Proc. of SIGMOD Int’l Conf. on Management of Data, pp. 347–358. ACM, New York (2004)Google Scholar
  3. 3.
    Belussi, A., Combi, C., Pozzani, G.: Towards a Formal Framework for Spatio-Temporal Granularities. In: Proc. of 15th Int’l Symp. on Temporal Representation and Reasoning, pp. 49–53. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  4. 4.
    Bertino, E., Camossi, E., Guerrini, G.: Access to Multigranular Temporal Objects. In: Christiansen, H., Hacid, M.-S., Andreasen, T., Larsen, H.L. (eds.) FQAS 2004. LNCS, vol. 3055, pp. 320–333. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Bettini, C., Jajodia, S., Wang, X.: Time Granularities in Databases, Data Mining, and Temporal Reasoning. Springer, Heidelberg (2000)CrossRefMATHGoogle Scholar
  6. 6.
    Camossi, E., Bertino, E., Guerrini, G., Mesiti, M.: Handling Expiration of Multigranular Temporal Objects. J. of Logic and Computation 14(1), 23–50 (2004)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Camossi, E., Bertolotto, M., Bertino, E.: A multigranular Object-oriented Framework Supporting Spatio-temporal Granularity Conversions. Int’l J. of Geographical Information Science 20(5), 511–534 (2006)CrossRefGoogle Scholar
  8. 8.
    Camossi, E., Bertolotto, M., Bertino, E.: Multigranular spatio-temporal models: Implementation challenges. In: Proc. of 16th SIGSPATIAL Int’l Conf. on Advances in Geographic Information Systems. ACM, New York (2008)Google Scholar
  9. 9.
    Garcia-Molina, H., Labio, W.J., Yang, J.: Expiring Data in a Warehouse. In: Proc. of 24th Int’l Conf. on Very Large Data Bases, pp. 500–511. ACM, New York (1998)Google Scholar
  10. 10.
    Jensen, C.S., Dyreson, C.E., Bohlen, M., Clifford, J., et al.: A Consensus Glossary of Temporal Database Concepts. In: Etzion, O., Jajodia, S., Sripada, S. (eds.) Dagstuhl Seminar 1997. LNCS, vol. 1399, pp. 367–405. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Orlando, S., Orsini, R., Raffaeta, A., Roncato, A., Silvestri, C.: Spatio-temporal Aggregations in Trajectory Data Warehouses. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2007. LNCS, vol. 4654, pp. 66–77. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Skyt, J., Jensen, C.S., Mark, L.: A Foundation for Vacuuming Temporal Databases. Data & Knowledge Engineering 44(1), 1–29 (2003)CrossRefMATHGoogle Scholar
  13. 13.
    Tao, Y., Papadias, D.: Historical spatio-temporal aggregation. ACM Transactions on Information Systems 23(1), 61–102 (2003)CrossRefGoogle Scholar
  14. 14.
    Toman, D.: Expiration of Historical Databases. In: Proc. of 8th Int’l Symp. on Temporal Representation and Reasoning. IEEE Computer Society, Los Alamitos (2001)Google Scholar
  15. 15.
    Yang, J., Widom, J.: Incremental computation and maintenance of temporal aggregates. The Int’l J. on Very Large Databases 12(3), 262–283 (2003)Google Scholar
  16. 16.
    Zhang, D., Gunopulos, D., Tsotras, V.J., Seeger, B.: Temporal and spatio-temporal aggregation over data streams using multiple time granularities. Information Systems 28(1-2), 61–84 (2003)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Elena Camossi
    • 1
  • Elisa Bertino
    • 2
  • Giovanna Guerrini
    • 3
  • Michela Bertolotto
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College Dublin, BelfieldDublin 4Ireland
  2. 2.CERIAS - Purdue UniversityUSA
  3. 3.DISI - Università degli Studi di GenovaGenovaItaly

Personalised recommendations