Symbolic Intersect Detection: A Method for Improving Spatial Intersect Joins

• Authors: Yun-Wu Huang;Matthew Jones;Elke A. Rundensteiner
• Venue: Geoinformatica
• Year: 1998

Due to the increasing popularity of spatial databases, researchershave focused their efforts on improving the query processing performance ofthe most expensive spatial database operation: the spatial join. While mostprevious work focused on optimizing the filter step, it has been discoveredrecently that, for typical GIS data sets, the refinement step of spatialjoin processing actually requires a longer processing time than the filterstep. Furthermore, two-thirds of the time in processing the refinement stepis devoted to the computation of polygon intersections. To address thisissue, we therefore introduce a novel approach to spatial join optimizationthat drastically reduces the time of the refinement step. We propose a newapproach called Symbolic Intersect Detection (SID) for early detection oftrue hits. Our SID optimization eliminates most of the expensive polygonintersect computations required by a spatial join by exploiting the symbolictopological relationships between the two candidate polygons and theiroverlapping minimum bounding rectangle. One important feature of our SIDoptimization is that it is complementary to the state-of-the-art methods inspatial join processing and therefore can be utilized by these techniques tofurther optimize their performance. In this paper, we also develop ananalytical cost model that characterizes SID’s effectiveness undervarious conditions. Based on real map data, we furthermore conduct anexperimental evaluation comparing the performance of the spatial joins withSID against the state-of-the-art approach. Our experimental results showthat SID can effectively identify more than 80% of the true hits withnegligible overhead. Consequently, with SID, the time needed for resolvingpolygon intersect in the refinement step is improved by over 50% overknown techniques, as predicted by our analytical model.