The database skyline query (or non-domination query) has a spatial form: Given a set P with n point sites, and a point set S of m locations of interest, a site $p\in P$ is a skyline point if and only if for each $q\in P\setminus \{p\}$, there exists at least one location $s\in S$ that is closer to p than to q. We reduce the problem of determining skyline points to the problem of finding sites that have non-empty cells in an additively weighted Voronoi diagram under a convex distance function. The weights of said Voronoi diagram are derived from the coordinates of the sites of P, while the convex distance function is derived from the set of locations S. In the two-dimensional plane, this reduction gives an $O((n+m)\log (n+m))$-time algorithm to find the skyline points.

数据库轮廓查询（或非支配查询）有一个空间形式：给定一组P与ň点位，点集小号的米感兴趣的位置，站点$p\in P$ 是且仅当每个 $q\in P\setminus \{p\}$，至少存在一个位置 $s\in \mathrm{小号}$更靠近p比q。我们将确定天际线点的问题减少到在凸距离函数下的加性加权Voronoi图中查找具有非空像元的站点的问题。所述Voronoi图的权重是从P的位置的坐标得出的，而凸距离函数是从位置S的集合得出的。在二维平面中，这种减少使$Ø（（ñ+米）\mathrm{日志}（ñ+米））$时间算法来查找天际线点。