Spatial OLAP and Map Generalization: Model and Algebra

Sandro Bimonte, Michela Bertolotto, Jérôme Gensel and Omar Boussaid
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Sandro Bimonte: TSCF, CEMAGREF, France
Michela Bertolotto: University College Dublin, Ireland
Jérôme Gensel: Equipe STEAMER, France
Omar Boussaid: University of Lyon 2, France

International Journal of Data Warehousing and Mining (IJDWM), 2012, vol. 8, issue 1, 24-51

Abstract: Map generalization can be used as a central component of Spatial Decision Support Systems to provide a simplified and more readable cartographic visualization of geographic information. Indeed, it supports the user mental process for discovering important and unknown geospatial relations, trends and patterns. Spatial OLAP (SOLAP) integrates spatial data into OLAP and data warehouse systems. SOLAP models and tools are based on the concepts of spatial dimensions and measures that represent the axes and the subjects of the spatio-multidimensional analysis. Although powerful under some respect, current SOLAP models cannot support map generalization capabilities. This paper provides the first effort to integrate Map Generalization and OLAP. Firstly the authors define all modeling and querying requirements to do this integration, and then present a SOLAP model and algebra that support map generalization concepts. The approach extends SOLAP spatial hierarchies introducing multi-association relationships, supports imprecise measures, and it takes into account spatial dimensions constraints generated by multiple map generalization hierarchies.

Date: 2012
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