A spatial data warehouse (SDW) consists of a set of materialized views defined over the source relations, either conventional, spatial, or both. Often, when compared to the traditional data warehouses, the cost of view materialization is more expensive with respect to both computation and space. This is because the spatial data is typically larger in size, which leads to high maintenance cost, and the spatial operations are more expensive to process. In this paper, we address the issue of optimizing the view materialization cost in an SDW. We build a cost model to measure the on-the-fly computation cost versus the space cost for spatial queries. We show that a spatial query can be represented in the form of the query-graph and propose three transformation rules, edge-elimination, query-splitting and query-joining, to selectively materialize spatial views. We present a greedy algorithm for materialized view selection so that the local cost optimality can be achieved.
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