Multidimensional Structures Dedicated to Continuous Spatiotemporal Phenomena

  • Taher Omran Ahmed
  • Maryvonne Miquel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3567)


Multidimensional structures or hypercubes are commonly used in OLAP to store and organize data to optimize query response time. The multidimensional approach is based on the concept of facts analyzed with respect to various dimensions. Dimensions are seen as axes of analysis forming a vector space in which each cell is located by a set of coordinates. In conventional multidimensional structures, dimensions have discrete values and are organized in different levels of hierarchies. However, when analysing natural phenomena like meteorology or pollution the discrete structures are not adequate. We will introduce mechanisms, based on interpolation, to spatial and temporal dimensions which will give the user the impression of navigating in a continuous hypercube. In this paper we go over the research issues associated with continuous multidimensional structures, we give some of their potentials and we propose a multidimensional model and some operations used for an OLAP of field-based data.


Interpolation Function Data Warehouse Dimension Level Continuous Dimension Aggregation Function 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Gupta, A., Sarawagi, S.: Modeling multidimensional databases. In: 13th Int. Conf. on Data Engineering (ICDE 1997), Birmingham, U. K, pp. 232–243 (1997)Google Scholar
  2. 2.
    Bedard Y.: Spatial OLAP. Vidéo-conférence. 2ème Forum annuel sur la R-D, Géomatique VI: Un monde accessible, Montréal, (consulted on 3/01/2005)
  3. 3.
    Blaschka, M., Sapia, C., Höfling, D.G.B.: Finding your way through multidimensional data models. In: Proc. of the Int. Workshop on Data Warehouse Design and OLAP Technology (DWDOT, in connection with DEXA), Vienna, Austria (1998)Google Scholar
  4. 4.
    Body, M., Miquel, M., Bedard, Y., Tchounikine, A.: Handling evolutions in multidimensional structures. In: Proc. of IEEE 19th Int. Conf. on Data Engineering (ICDE), India, pp. 581–591 (2003)Google Scholar
  5. 5.
    Cabibbo, L., Torlone, R.: Querying Multidimensional Databases. In: Proc. of the 6th Int. Workshop on Database Programming Languages (DBPL), Estes Park, Colorado, USA (1997)Google Scholar
  6. 6.
    Cabibbo, L., Torlone, R.: A Logical Approach to Multidimensional Databases. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, p. 183. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  7. 7.
    Franklin, C.: An Introduction to geographic Information Systems: Linking Maps to databases. Database, 13-21 (1992)Google Scholar
  8. 8.
    Gordillo, S.: Modelling and manipulation of spatiotemporal phenomena. PhD. Thesis, Claude Bernard University Lyon I, France (2001) (in French)Google Scholar
  9. 9.
    Gyssens, M., Lakshmanan, L.V.S.: A Foundation for Multi-Dimensional Databases. In: Proc. of 23rd Int. Conf. on Very Large Data Bases, Athens, Greece (1997)Google Scholar
  10. 10.
    Inmon, W.H.: Building the data warehouse. John Wiley and Sons, Chichester (1992)Google Scholar
  11. 11.
    Kemp, Z., Lee, H.: A Multidimensional Model for Exploratory Spatiotemporal Analysis. In: Proc. of the 5th Int. Conf. on GeoComputation,, University of Greenwich, UK (2000)Google Scholar
  12. 12.
    Miquel, M., Bedard, Y., Brisebois, A., Pouliot, J., Marchand, P., Brodeur, J.: Modeling multidimensional spatiotemporal data warehouse in a context of evolving specifications. In: Symposium on Geospatial Theory, Processing and Applications, Ottawa (2002)Google Scholar
  13. 13.
    Paradias, D., Tao, Y., Zhang, J., Mamoulis, N., Shen, Q., Sun, J.: Indexing and retrieval of historical aggregate information about moving objects. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 25(2), 10–17 (2002)Google Scholar
  14. 14.
    Rivest, S., Bedard, Y., Proulx, M.J., Nadeau, M.: SOLAP: A new type of user interface to support spatiotemporal multidimensional data exploration and analysis. In: Proc. of ISPRS workshop on Spatial, Temporal and Multidimensional Data Modeling and Analysis, Québec City, Canada (2003)Google Scholar
  15. 15.
    Shanmugasundaram, J., Fayyad, U.M., Bradely, P.S.: Compressed data cubes for OLAP aggregate query approximation on continuous dimensions. In: KDD 1999, pp. 223–232. ACM Press, New York (1999)Google Scholar
  16. 16.
    Stefanovic, N., Han, J., Koperski, K.: Object-based selective materialization for efficient implementation of spatial data cubes. IEEE Transaction on Knowledge and Data Engineering 12(6), 938–957 (2000)CrossRefGoogle Scholar
  17. 17.
    Vassiliadis, P.: Modeling Multidimensional Databases, Cubes and Cube Operations. In: Proc. of the 10th Int. Conf. on Sci. and Stat. Database Management (SSDBM), Italy (1998)Google Scholar
  18. 18.
    Vassiliadis, P., Sellis, T.: A Survey of Logical Models for OLAP Databases. SIGMOD Record 28(4) (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Taher Omran Ahmed
    • 1
  • Maryvonne Miquel
    • 1
  1. 1.LIRIS – INSA de Lyon, Bât. Blaise Pascal 501.302VilleurbanneFrance

Personalised recommendations