Abstract
This paper studies a spatial group-by query over complex polygons. Groups are selected from a set of non-overlapping complex polygons, typically in the order of thousands, while the input is a large-scale dataset that contains hundreds of millions or even billions of spatial points. Given a set of spatial points and a set of polygons, the spatial group-by query returns the number of points that lie within boundaries of each polygon. This problem is challenging because real polygons (like counties, cities, postal codes, voting regions, etc.) are described by very complex boundaries. We propose a highly-parallelized query processing framework to efficiently compute the spatial group-by query. Our experimental evaluation with real data and queries has shown significant superiority over all existing techniques.
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