SGPAC: Generalized Scalable Spatial GroupBy Aggregations over Complex Polygons
SGPAC: Generalized Scalable Spatial GroupBy Aggregations over Complex Polygons
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SGPAC：通过复杂多边形聚合的广义可扩展空间组
DOI：
10.1007/s10707-023-00491-8
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发表时间：
2023
期刊：
影响因子：
2
通讯作者：
Tsotras, Vassilis J.
中科院分区：
文献类型：
--
作者：
Abdelhafeez, Laila;Magdy, Amr;Tsotras, Vassilis J.
本文研究了复多边形的空间群查询问题。给定一组空间点和一组多边形，空间分组查询返回位于每个多边形边界内的点的数量。组是从一组不重叠的复杂多边形中选择的，通常以数千为数量级，而输入是包含数亿甚至数十亿空间点的大规模数据集。这个问题具有挑战性，因为真实的多边形（如县、市、邮政编码、投票区域等）是由非常复杂的边界描述的。提出了一种高度并行化的查询处理框架，以有效地计算高倾斜空间数据的空间分组查询。我们还提出了一个有效的查询优化器，它可以根据查询多边形自适应地分配适当的处理方案。我们对真实数据和查询的实验评估显示出比所有现有技术显著的优势。
This paper studies thespatial group-by queryover complex polygons. Given a set of spatial points and a set of polygons, the spatial group-by query returns the number of points that lie within the boundaries of each polygon. 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. 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 on highly skewed spatial data. We also propose an effective query optimizer that adaptively assigns the appropriate processing scheme based on the query polygons. Our experimental evaluation with real data and queries has shown significant superiority over all existing techniques.