Object-based selective materialization for efficient implementation of spatial data cubes

With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, i t is important to study the methods for spatial data warehousing and OLAP o f spatial data. In this paper, we study methods for spatial OLAP, by integr ation of nonspatial OLAP methods with spatial database implementation techn iques. A spatial data warehouse model, which consists of both spatial and n onspatial 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 sel ective materialization of the spatial objects resulted from spatial OLAP op erations. The focus of our study is on a method for spatial cube constructi on, called object-based selective materialization, which is different from cuboid-based selective materialization proposed in previous studies of nons patial data cube construction. Rather than using a cuboid as an atomic stru cture during the selective materialization, we explore granularity on a muc h finer level, that of a single cell of a cuboid. Several algorithms are pr oposed for object-based selective materialization of spatial data cubes and the performance study has demonstrated the effectiveness of these techniqu es.