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.
Highlights
We investigate the efficient processing of k-farthest neighbor join queries in spatial networks where the distance between two points is defined by the length of the shortest path connecting them
Note that the CNLJOPT algorithm evaluates k-farthest neighbor (kFN) queries at border points of query clusters, the CNLJNV algorithm evaluates kFN queries at end points of query segments, and the baseline algorithm evaluates kFN queries at query points
The experimental results demonstrated that the cluster nested loop join (CNLJ) algorithm runs up to 50.8 times faster than the conventional join algorithms and that the CNLJ algorithm better scales with the numbers of both data and query points than the conventional join algorithms
Summary
We investigate the efficient processing of k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is defined by the length of the shortest path connecting them. This paper proposes a cluster nested loop join (CNLJ) algorithm for spatial networks to solve the problem of efficiently processing kFN join queries. The CNLJ algorithm has several advantages over the traditional solution: (1) it clusters query points (data points) using the spatial network connection for the shared computation, (2) it quickly retrieves candidate data points at once for clustered query points, and (3) it does not retrieve candidate data points for each query point separately. This paper presents a cluster nested loop join algorithm for quickly evaluating spatial network kFN join queries.
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