Approximations for a Multi-Step Processing of Spatial Joins

The basic concept for processing spatial joins consists of two steps: First, the spatial join is performed on the minimum bounding rectangles of the objects by using a spatial access method. This step provides a set of candidates which consists of answers (hits) and non-answers (false hits). In the second step, the exact geometry of the candidates is transferred from secondary storage into main memory and is tested against the join predicate. This step is called refinement step. It causes the main cost for computing a spatial join. In this paper, we introduce an additional filter step in order to reduce the cost of the refinement step. In this filter step more sophisticated approximations are used to identify hits as well as to filter out false hits from the set of candidates. For this purpose, we investigate various types of conservative and progressive approximations. The performance of the approximation approach is evaluated with data sets from real cartographic applications. The results show that this approach considerably reduces the total execution time of the spatial join.