Advertisement

Defining Spatio-Temporal Granularities for Raster Data

  • Gabriele Pozzani
  • Esteban Zimányi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6121)

Abstract

The notion of granularity is used in several areas of computing. In temporal databases, granularity relates to the fact that the time frame associated to an event of interest (e.g., an accident) can be envisaged at several levels of detail (e.g., hour, day, month, etc.). Similarly, granularity in data warehousing is the level of detail at which facts (e.g., sales) are captured in dimensions (e.g., product, store, and day). However, there is no commonly-agreed definition of spatial or spatio-temporal granularities. Sometimes, the term spatial granularity is confounded with multiple resolutions. Further, the few proposals about them are mainly focused on the vector data model. In this paper, we define spatial and spatio-temporal granularities for raster data models. In our framework, relations and operations between spatial and spatio-temporal granularities are also defined.

Keywords

Space Domain Raster Data Poral Granularity Temporal Granularity Time Granule 
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.
    Belussi, A., Combi, C., Pozzani, G.: Formal and conceptual modeling of spatio-temporal granularities. In: Proceedings of the International Database Engineering and Applications Symposium, pp. 275–283. ACM (2009)Google Scholar
  2. 2.
    Camossi, E., Bertolotto, M., Bertino, E.: A multigranular object-oriented framework supporting spatio-temporal granularity conversions. Int. J. Geogr. Inf. Sci. 20(5), 511–534 (2006)CrossRefGoogle Scholar
  3. 3.
    Cattell, R.G.G., Berler, D.K.B.M., Eastman, J., Jordan, D., Russell, C., Schadow, O., Stanienda, T., Velez, F. (eds.): The Object Data Standard: ODMG 3.0. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar
  4. 4.
    Erwig, M., Schneider, M.: Partition and Conquer. In: Frank, A.U. (ed.) COSIT 1997. LNCS, vol. 1329, pp. 389–407. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  5. 5.
    Frank, A.U.: Map Algebra Extended with Functors for Temporal Data. In: Akoka, J., Liddle, S.W., Song, I.-Y., Bertolotto, M., Comyn-Wattiau, I., van den Heuvel, W.-J., Kolp, M., Trujillo, J., Kop, C., Mayr, H.C. (eds.) ER Workshops 2005. LNCS, vol. 3770, pp. 194–207. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Güting, R.H., Böhlen, M.H., Erwig, M., Jensen, C.S., Lorentzos, N.A., Schneider, M., Vazirgiannis, M.: A foundation for representing and querying moving objects. ACM Trans. Database Syst. 25(1), 1–42 (2000)CrossRefGoogle Scholar
  7. 7.
    Malinowski, E., Zimányi, E.: Advanced data warehouse design: From conventional to spatial and temporal applications. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  8. 8.
    McKenney, M., Schneider, M.: Spatial Partition Graphs: A Graph Theoretic Model of Maps. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 167–184. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Mennis, J., Tomlin, C.D.: Cubic map algebra functions for spatio-temporal analysis. Cartogr. and Geogr. Inform. 32(1), 17–32 (2005)CrossRefGoogle Scholar
  10. 10.
    Ning, P., Wang, X.S., Jajodia, S.: An algebraic representation of calendars. Ann. Math. Artif. Intel. 36(1-2), 5–38 (2002)CrossRefzbMATHGoogle Scholar
  11. 11.
    Shekhar, S., Xiong, H. (eds.): Encyclopedia of GIS. Springer, Heidelberg (2008)Google Scholar
  12. 12.
    Tomlin, C.D., Berry, J.K.: A mathematical structure for cartographic modeling in environmental analysis. In: Proceedings of the 39th Symposium of the American Congress on Surveying and Mapping, pp. 269–283 (1979)Google Scholar
  13. 13.
    Wang, S., Liu, D.: Spatio-temporal Database with Multi-granularities. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 137–146. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gabriele Pozzani
    • 1
  • Esteban Zimányi
    • 2
  1. 1.Dept. of Computer ScienceUniversity of VeronaItaly
  2. 2.Dept. of Computer & Decision Engineering (CoDE)Université Libre de BruxellesBelgium

Personalised recommendations