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<p><b>Abstract</b>—With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integration of nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies proposed, including approximation and selective materialization of the spatial objects resulted from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called <it>object-based selective materialization</it>, which is different from <it>cuboid-based selective materialization</it> proposed in previous studies of nonspatial data cube construction. Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level, that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes and the performance study has demonstrated the effectiveness of these techniques.</p>
Data warehouse, data mining, online analytical processing (OLAP), spatial databases, spatial data analysis, spatial OLAP.

J. Han, N. Stefanovic and K. Koperski, "Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes," in IEEE Transactions on Knowledge & Data Engineering, vol. 12, no. , pp. 938-958, 2000.
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