Multi-resolution Algorithms for Building Spatial Histograms

Selectivity estimation of queries not only provides useful information to the query processing optimization but also may give users a preview of processing results. In this paper, we investigate the problem of selectivity estimation in the context of a spatial dataset. Specifically, we focus on the calculation of four relations, contains, contained, overlap and disjoint, between data objects and a query rectangle using Euler -histograms. We first provide a multi-resolution algorithm which can lead to the exact solutions but at the cost of storage space. To conform to a given storage space, we also provide an approximate algorithm based on a hybrid multi-resolution paradigm. Our experiments suggest that our algorithms greatly out-perform the existing techniques.