Abstract:

In a decision-making context, multidimensional geospatial databases are very important. They often represent data coming from heterogeneous and evolving sources. Evolution of multidimensional structures makes difficult, even impossible answering to temporal queries, since relationships between different temporal versions of spatial cubes often remain unknown. This paper proposes a semantic mapping model based on a semantic similarity model to establish semantic relations between spatial regions of data cubes. The proposed model integrates several similarity components adapted to the different hierarchical levels of spatial dimensions in multidimensional databases. Then the semantic mapping model implementation has been coupled with the SOLAP (Spatial On-Line Analytical Processing) tool which allows visualizing semantic mapping results between spatial regions. The developed prototype shows to be very useful for supporting decision making which is based on analysis of spatial variability and evolution.