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

Abstract

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 locationbased 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. INDEX TERMS Spatial databases, group processing, multiple k-farthest neighbor query, road network.

The Efficient Processing of Moving k-Farthest Neighbor Queries in Road Networks

“…Therefore, in this study, MOFA was thoroughly analyzed and an empirical evaluation was performed. This study also differs from our previous studies [20,21] in several aspects. Cho [20] considered kFN join queries in a spatial network.…”

“…Cho [20] considered kFN join queries in a spatial network. The kFN join query focuses on evaluating a snapshot of the kFN query for each query point in Q. Cho and Attique [21] presented the group processing of multiple kFN (GMP) algorithms to efficiently process multiple kFN queries in road networks. The GMP algorithm exploits shared computation techniques to rapidly process snapshot kFN queries for multiple query points with distinct query conditions.…”

“…The GMP algorithm exploits shared computation techniques to rapidly process snapshot kFN queries for multiple query points with distinct query conditions. Unlike our previous studies [20,21] on snapshot kFN queries, this study focuses on computing valid segments for continuous kFN queries of a moving query point.…”

“…Space Domain Query Type [3,4,8,9,13] Euclidean space Reverse FN query [1,4,[10][11][12]14] Euclidean space FN query [2] Euclidean space Aggregate FN query [5,7] Road network Reverse FN query [6] Road network Aggregate FN query [20] Road network FN join query [21] Road network Multiple FN query This study Road network Moving FN query…”

A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

“…(Spatial network [ 3 , 9 , 11 , 25 , 26 , 41 , 50 , 51 ]) . A dynamic spatial network can be described as a dynamic weighted graph , where V, E, and W indicate the vertex set, edge set, and edge distance matrix, respectively.…”