A spatial OLAP can be characterised as a practical union of OLAP analysis and geographic mapping. A spatial OLAP query has a spatial confinement along with the conventional non-spatial predicate. An existing framework we opt for is to convert a spatial OLAP query into a set of queries for a general-purpose ROLAP engine. However, little has been done at the query optimization level, once the queries are submitted to the query engine. This thesis introduces three query engines on an experimental MOLAP system. The first is the implementation of the framework in the MOLAP context. The second increases the efficiency by adopting a novel merging technique to screen out many useless queries. The third does all aggregation on the fly, which outperforms the first two query engines by a wide margin under many circumstances. Detailed experimental performance data are presented, using a real-life database with 1/3 million of spatial objects.
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