Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.

中文翻译：

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动态空间网络中空间邻近查询处理的统一方法

最近邻 (NN) 和范围 (RN) 查询是空间数据库中的基本查询类型。在本研究中，我们将 NN 和 RN 查询的集合称为空间邻近度 (SP) 查询。在高峰时段，基于位置的服务 (LBS) 需要快速处理同时到达的 SP 查询。可以通过增加LBS服务器的数量来实现及时处理；然而，这也增加了服务成本。现有解决方案按顺序评估 SP 查询；因此，此类解决方案涉及不必要的距离计算。本研究提出了一种统一批处理算法（UBA），可以有效地处理动态空间网络中的 SP 查询。对于提议的 UBA，两点之间的距离由连接它们的最短路径上的旅行时间表示。最短的旅行时间根据交通状况经常变化。提议的 UBA 的目标是避免对附近 SP 查询进行不必要的距离计算。因此，UBA 对附近的 SP 查询进行聚类，并利用查询聚类的共享距离计算。使用真实世界路线图的广泛评估证明了 UBA 与最先进的顺序解决方案相比的优越性和可扩展性。