This paper presents a novel query for spatial databases, called reverse nearest neighborhood (RNH) query, to discover the neighborhoods that find a query facility as their nearest facility among other facilities in the dataset. Unlike a reverse nearest neighbor (RNN) query, an RNH query emphasizes on group of users instead of an individual user. More specifically, given a set of user locations $U$, a set of facility locations $F$, a query location $q$, a distance parameter $\rho$ and a positive integer $k$, an RNH query returns all $\rho$-radius circles $C$ enclosing at least $k$ users $u\in U$, called neighborhoods (NH) such that the distance between $q$ and $C$ is less than the distance between $C$ and any other facility $f\in F$. The RNH queries might have many practical applications including on demand facility placement and smart urban planning. We present an efficient approach for processing RNH queries on location data using R-tree based data indexing. In our approach, first we retrieve candidate RNH users by an efficient bound, prune and refine technique. Then, we incrementally discover RNHs of a query facility from these candidate RNH users. We also present the variants of RNH queries in spatial databases and propose solutions for them. We validate our approach by conducting extensive experiments with real datasets.

本文提出了一种空间数据库，一个新的查询，称为[R EVERSE ň earest邻居^ h OOD（RNH）查询，发现，找到一个查询功能，如数据集等设施的最近的设施的社区。与反向最近邻居（RNN）查询不同，RNH查询强调用户组而不是单个用户。更具体地说，给定一组用户位置$ü$，一组设施位置 $F$，查询位置 $q$，距离参数 $\rho$ 和一个正整数 $ķ$，RNH查询返回所有 $\rho$-半径圆 $C$ 至少包围 $ķ$ 使用者 $ü\in ü$称为Ñ eighbor ħ水灾（NH），使得之间的距离$q$ 和 $C$ 小于之间的距离 $C$ 和其他设施 $F\in F$。RNH查询可能具有许多实际应用，包括按需设施放置和智能城市规划。我们提出了一种有效的方法，用于使用基于R树的数据索引处理位置数据上的RNH查询。在我们的方法中，首先，我们通过有效的绑定，修剪和细化技术来检索候选RNH用户。然后，我们从这些候选RNH用户中逐步发现查询工具的RNH。我们还介绍了空间数据库中RNH查询的变体，并提出了解决方案。我们通过对真实数据集进行广泛的实验来验证我们的方法。