Hierarchical metadata driven view dependency in spatial data warehouses

Recently, tremendous growth in the collection and use of spatial information can be seen. To efficiently and effectively analyze the spatial data and their interactive relationships, there is a need for a spatial data warehouse (SDW) that should be carefully constructed to support the spatial data analysis for decision support purposes. In an SDW, spatial objects represented as central measures are described by spatial metadata represented as dimensions, and some of dimensions have concept hierarchies manually or automatically built. The spatial metadata with built-in concept hierarchies present the spatial data at different abstract levels, which defines a sequence of mappings from lower-level views to higher-level or more general views. In this paper, we investigate the issue of using commonly used spatial metadata to construct a spatial view dependency framework in a 3D platform, which is used to express and quantify dependencies among spatial views. We also show the extendibility of the 3D framework if more spatial metadata considered or more complex concept hierarchies involved. We indicate that a spatial view dependency is mainly used for selective materialization of views to reduce query response time, which is a major query optimization technique for an SDW.