Efficient spatial queries over complex polygons with hybrid representations

One major goal of spatial query processing is to mitigate I/O costs and minimize the search space. However, geometric computation can be heavy-duty for spatial queries, in particular for complex geometries such as polygons with many edges based on a vector-based representation. Many past techniques have been provided for spatial partitioning and indexing, which are mainly built on minimal bounding boxes or other approximation methods and are not optimized for reducing geometric computation. In this paper, we propose a novel vector-raster hybrid approach through rasterization, where rich pixel-centric information is preserved to help not only filter out more candidates but also reduce geometry computation load. Based on the hybrid model, we implement four typical spatial queries, which can be generalized for other types of spatial queries. We also propose cost models to estimate the latency for those query types. Our experiments demonstrate that the hybrid model can boost the performance of spatial queries on complex polygons by up to one order of magnitude.