Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
CSOLAP (Continuous Spatial On-Line Analytical Processing)

Authors: Taher Omran Ahmed, Abdullatif Mihdi Buras


Decision support systems are usually based on multidimensional structures which use the concept of hypercube. Dimensions are the axes on which facts are analyzed and form a space where a fact is located by a set of coordinates at the intersections of members of dimensions. Conventional multidimensional structures deal with discrete facts linked to discrete dimensions. However, when dealing with natural continuous phenomena the discrete representation is not adequate. There is a need to integrate spatiotemporal continuity within multidimensional structures to enable analysis and exploration of continuous field data. Research issues that lead to the integration of spatiotemporal continuity in multidimensional structures are numerous. In this paper, we discuss research issues related to the integration of continuity in multidimensional structures, present briefly a multidimensional model for continuous field data. We also define new aggregation operations. The model and the associated operations and measures are validated by a prototype.

Keywords: Continuous Data, Data warehousing, DecisionSupport, SOLAP

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1499


[1] Ahmed, T. O. and Miquel, M. Multidimensional Structures Dedicated to Continuous Spatiotemporal Phenomena. Proc 22nd British National Conf. on Databases (BNCOD22), Sunderland. 2005, 29-40.
[2] AIRAPRIF, Monitoring the quality of air in Ile de France.
[3] Bédard, Y. "Spatial OLAP." Vidéo-conférence. 2ème Forum annuel sur la R-D, Géomatique VI: Un monde accessible, 13-14 Nov., 1997, Montréal
[4] Bédard, Y., Merrett, T. and Han, J. Fundamentals of Spatial Data Warehousing for Geographic Knowledge Discovery in Geographic Data Mining and Knowledge Discovery. Research Monographs in GIS series edited by Peter Fisher and Jonathan Raper. 2001. 53-73.
[5] Bédard, Y., Proulx, M.J. and Rivest, S. Enrichissement du OLAP pour l'analyse géographique: exemples de réalisations et différentes possibilités technologiques. 1ere journée francophone sur les entrep├┤ts de données et l'analyse en ligne, Lyon, 2005. In French.
[6] Cowen, D. J. "GIS versus CAD versus DBMS: What are the differences?" Fotogrammetric Engineering and Remote Sensing, 54, 1988, 1551-1555.
[7] Devillers, R., Gervais, M., Bédard, Y. and Jeansoulin, R. 2002. Spatial Data Quality: From Metadata to Quality Indicators and Contextual End-user Manuel, in OEEPE-ISPRS, Istanbul. Joint Workshop on Spatial Data Quality.
[8] Franklin, C. An Introduction to geographic Information Systems: Linking Maps to databases. Database, 1992, 13-21.
[9] Filho, J. L. and Iochpe, C. 1999. "Specifying analysis patterns for geographic databases on the basis of a conceptual framework." In the Proc. of the 7th ACM int. symposium on Advances in GIS, Kansas City, Missouri, United States, pp 7 - 13.
[10] Galton, A. Integrating Fields and Objects in Geographic Information Science. Workshop on Fundamental Issues in Spatial and Geographic Ontologies, Switzerland, 2003.
[11] Goodchild, M. Geographical information science. International Journal of GIS, 2003, 6, 31-45.
[12] Gordillo, S. "Modélisation et manipulation de phénomènes continus spatio-temporels." Thèse de doctorat. Université Claude Bernard Lyon I. 2001. In French.
[13] Hasenauer, H., Haslik, I., Rosenthaler, R., Pernul, G. and Stangl, D. Conceptual framework of a data warehouse for the National park Hohe Tauern. Proc. 13th Int. Symposium "Informatik f├╝r den Umweltschutz" der Gesellschaft f├╝r Informatik (GI), Magdeburg, 1999, 478-480.
[14] Inmon, W. H. Building the Data Warehouse. John Wiley and sons. 1992.
[15] Kemp, K. and Vckovski, A. Towards an Ontology of Fields. Proc. of the 3rd Int. Conf. on GeoComputation, Bristol, UK, 1998.
[16] Kouba, Z., Matousek, K. and Milkovsky, P. On Data Warehouse and GIS integration. Proc. of the 11th Int. Conf. and Workshop on Database and Expert Systems Applications, Greenwich, 2000, 604- 613.
[17] Marchand, P., Brisebois, A., Bedard, Y. and Edwards, G. Implementation and Evaluation of a Hypercube-Based Method for Spatiotemporal Exploration and Analysis. ISPRS journal of photogrammetry and remote sensing. 59 (1,2), 2004, 6-20.
[18] Mennecke, B. and Higgins, G. 1999. "Spatial Data in the Data Warehouse: A Nomenclature for Design and Use," the 5th Ann. Americas Conf. on Information Systems. pp. 274 - 276.
[19] Morgan, D. G. and Glover, T. Distributing Data Ownership: The Northwestern Geospatial Data Network. GIS 2001. Vancouver, B.C., February 10-22.
[20] Mostaccio, C. A. 2003. "Organisation physique des bases de données pour les champs continues." Thèse de doctorat, Université Claude Bernard Lyon I, France. In French.
[21] Pariente, D. Estimation, modélisation et langage de déclaration et de manipulation de champs spatiaux continus." Thèse de doctorat. Institut National des Science Appliquées de Lyon. 1994. In French.
[22] Rivest, S., Bédard, Y. and Marchand, P. Towards Better Support for Spatial Decision Making: Defining the Characteristics of Spatial On- Line Analytical Processing (SOLAP). Geomatica, the journal of the Canadian Institute of Geomatics, 55, 2001, 539-555.
[23] Rivest, S., Bedard, Y., Proulx, M.J. and Nadeau, M. SOLAP: A New Type of User Interface to Support Spatiotemporal Multidimensional Data Exploration and Analysis. Proc. of ISPRS workshop on Spatial, Temporal and Multi-Dimensional Data Modeling and Analysis, Québec City, Canada, 2003.
[24] Schabenberger, O. and Gotway, C. A. Statistical Methods for Spatial Data Analysis. Chapman & Hall/CRC Pres. 2005.
[25] Shanmugasundaram, J., Fayyad, U. M. and Bradely, P. S. Compressed data cubes for OLAP Aggregate Query Approximation on Continuous Dimensions. Proc. of the 5th ACM SIGKGG International Conf. on Discovery and Data Mining (KDD99), New York, 1999, 223-232.
[26] Shen, S., Dzikowski, P., Li, G. and Griffith, D. Interpolation of 1961- 97 Daily Temperature and Precipitation Data onto Alberta Polygons of Ecodistrict and Soil Landscapes of Canada. Journal of. Applied Meteorology, 40, 2162 - 2176.
[27] Staudt, M., Vaduva, A. and Vetterli, T. The Role of Metadata for Data Warehousing." Technical Report 99.06, Department of Information Technology, University of Zurich, September, 1999.
[28] Stefanovic, N., Han, J. and Koperski, K. Object-based Selective Materialization for Efficient Implementation of Spatial Data Cubes. IEEE Transactions on Knowledge and Data Engineering, 12(6), 2000, 938 - 957.
[29] Tan, X. Data Warehousing and Its Potential Using in Weather Forecast. Proc. 22nd Int. Conf. on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology. Atlanta, GA. 2006.
[30] Tchounikine, A., Miquel, M., Laurini, R., Ahmed, T.O., Bimonte, S. and Baillot, V. Panorama de travaux autour de l-intégration de données spatio-temporelles dans les hypercubes. Revue des Nouvelles Technologies de l'Information (RNTI), Editions Cepaduès, numéro spécial, Juin, 2005. In French.
[31] Vassiliadis, P. Modeling Multidimensional Databases, Cubes and Cube Operations. Proc. of the 10th Int. Conf. on Scientific and Statistical Database Management (SSDBM), Capri, Italy, 1998.
[32] Veregin, H. 1999. "Data Quality Parameters." In Geographical Information Systems, Vol. Principles and Technical Issues (Eds, Longley, P. A., Goodchild, M. F., Maguire, D. J. and Rhind, D. W.) John Wiley & Sons, Inc., pp. 177-189.