Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks
<|reference_start|>Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks: 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.<|reference_end|>
@article{wang2024simpler,
    title={Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road
  Networks},
    author={Yiqi Wang, Long Yuan, Wenjie Zhang, Xuemin Lin, Zi Chen, Qing Liu},
    journal={arXiv preprint arXiv:2408.05432},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05432},
    primaryClass={cs.DB}
}