BRkNN-light: Batch Processing of Reverse k-Nearest Neighbor Queries for Moving Objects on Road Networks
Abstract
The Reverse -Nearest Neighbor (RNN) query over moving objects on road networks seeks to find all moving objects that consider the specified query point as one of their nearest neighbors. In location based services, many users probably submit RNN queries simultaneously. However, existing methods largely overlook how to efficiently process multiple such queries together, missing opportunities to share redundant computations and thus reduce overall processing costs. To address this, this work is the first to explore batch processing of multiple RNN queries, aiming to minimize total computation by sharing duplicate calculations across queries. To tackle this issue, we propose the BRNN-Light algorithm, which uses rapid verification and pruning strategies based on geometric constraints, along with an optimized range search technique, to speed up the process of identifying the RNNs for each query. Furthermore, it proposes a dynamic distance caching mechanism to enable computation reuse when handling multiple queries, thereby significantly reducing unnecessary computations. Experiments on multiple real-world road networks demonstrate the superiority of the BRNN-Light algorithm on the processing of batch queries.
Cite
@article{arxiv.2512.23298,
title = {BRkNN-light: Batch Processing of Reverse k-Nearest Neighbor Queries for Moving Objects on Road Networks},
author = {Anbang Song and Ziqiang Yu and Wei Liu and Yating Xu and Mingjin Tao},
journal= {arXiv preprint arXiv:2512.23298},
year = {2025}
}