Group Processing of Multiple k-Farthest Neighbor Queries in Road Networks

Advances in mobile technologies and map-based applications enables users to utilize sophisticated spatial queries, including k-nearest neighbor and shortest path queries. Often, location-based servers are used to handle multiple simultaneous queries because of the popularity of map-based applications. This study focuses on the efficient processing of multiple concurrent k-farthest neighbor (kFN) queries in road networks. For a positive integer k, query point q, and set of data points P, a kFN query returns k data points farthest from the query point q. For addressing multiple concurrent spatial queries, traditional location-based servers based on one-query-at-a-time processing are unsuitable owing to high redundant computation costs. Therefore, we propose a group processing of multiple kFN (GMP) algorithm to process multiple kFN queries in road networks. The proposed GMP algorithm uses group computation to avoid the redundant computation of network distances between the query and data points. The experiments using real-world roadmaps demonstrate the proposed solution's effectiveness and efficiency.