Cluster Nested Loop k-Farthest Neighbor Join Algorithm for Spatial Networks

Abstract

This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions.

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.…”

“…This study also differs from our previous studies [20,21] in several aspects. 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. 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…”