Efficient Exact k-Flexible Aggregate Nearest Neighbor Search in Road Networks Using the M-tree

TL;DR

This paper introduces the first exact k-flexible aggregate nearest neighbor search algorithm in road networks utilizing the M-tree, significantly reducing index node accesses compared to previous Euclidean-based methods.

Contribution

The study presents a novel exact FANN algorithm based on the M-tree for road networks, outperforming existing methods in efficiency by leveraging shortest-path distances.

Findings

Algorithm reduces unnecessary index node accesses

Outperforms IER-kNN by up to 6.92 times

No false drops in the proposed method

Abstract

This study proposes an efficient exact k-flexible aggregate nearest neighbor (k-FANN) search algorithm in road networks using the M-tree. The state-of-the-art IER-kNN algorithm used the R-tree and pruned off unnecessary nodes based on the Euclidean coordinates of objects in road networks. However, IER-kNN made many unnecessary accesses to index nodes since the Euclidean distances between objects are significantly different from the actual shortest-path distances between them. In contrast, our algorithm proposed in this study can greatly reduce unnecessary accesses to index nodes compared with IER-kNN since the M-tree is constructed based on the actual shortest-path distances between objects. To the best of our knowledge, our algorithm is the first exact FANN algorithm that uses the M-tree. We prove that our algorithm does not cause any false drop. In conducting a series of experiments using various real road network datasets, our algorithm consistently outperformed IER-kNN by up to 6.92 times.