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.

空间查询处理的主要目标之一是降低 I/O 成本并最小化搜索空间。然而，几何计算对于空间查询来说可能是繁重的，特别是对于复杂的几何形状，例如基于矢量表示的具有许多边的多边形。过去已经提供了许多用于空间分区和索引的技术，这些技术主要建立在最小边界框或其他近似方法上，并且没有针对减少几何计算进行优化。在本文中，我们通过光栅化提出了一种新颖的矢量光栅混合方法，其中保留了丰富的以像素为中心的信息，不仅有助于过滤出更多候选者，而且还减少了几何计算负载。基于混合模型，我们实现了四种典型的空间查询，它们可以推广到其他类型的空间查询。我们还提出了成本模型来估计这些查询类型的延迟。我们的实验表明，混合模型可以将复杂多边形的空间查询性能提高一个数量级。