Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks
Yiqi Wang, Long Yuan, Wenjie Zhang, Xuemin Lin, Zi Chen, Qing Liu
TL;DR
The paper targets the top-$k$ nearest neighbors problem on road networks, where direct Dijkstra search and complex indexing yield high query delays and large index footprints. It introduces a minimalist KNN-Index that stores, for every vertex $u$, the top-$k$ neighbors ${\mathsf{V_k}}(u)$, enabling $O(k)$ query time and a bounded index size of $O(n \cdot k)$, and couples this with a bridge-neighbor preserved graph (BN-Graph) to enable efficient computation sharing. Two construction strategies are proposed: a bottom-up, computation-sharing approach and a bidirectional construction method, both designed to maximize reuse of intermediate results and reduce distance computations; incremental maintenance is also provided for candidate object updates. Extensive experiments on real road networks show that the proposed approach dramatically reduces index size, accelerates indexing and query processing by up to two orders of magnitude, and remains scalable as graph size grows, outperforming TEN-Index and GLAD across key metrics.
Abstract
Top-k Nearest Neighbors (kNN) problem on road network has numerous applications on location-based services. As direct search using the Dijkstra's algorithm results in a large search space, a plethora of complex-index-based approaches have been proposed to speedup the query processing. However, even with the current state-of-the-art approach, long query processing delays persist, along with significant space overhead and prohibitively long indexing time. In this paper, we depart from the complex index designs prevalent in existing literature and propose a simple index named KNN-Index. With KNN-Index, we can answer a kNN query optimally and progressively with small and size-bounded index. To improve the index construction performance, we propose a bidirectional construction algorithm which can effectively share the common computation during the construction. Theoretical analysis and experimental results on real road networks demonstrate the superiority of KNN-Index over the state-of-the-art approach in query processing performance, index size, and index construction efficiency.