Reverse k nearest neighbor (RkNN) queries are fundamental in spatial databases, location-based analytics, and recommendation systems. Existing state-of-the-art techniques rely on spatial pruning supported by R-trees and their variants. However, their pruning effectiveness degrades significantly in challenging scenarios where the number of facilities is small, the user population is dense, or the value of k is large. To overcome these limitations, this work reformulates the RkNN query problem in two-dimensional geometric spaces as a graphics ray-casting problem, where users are modeled as rays and facilities are represented as geometric primitives. Based on this formulation, the first algorithm and implementation exploiting dedicated hardware ray-tracing cores on modern GPUs are developed. This novel approach preserves strong filtering performance even for large values of k, dense user populations, and highly sparse facility distributions. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art algorithms across diverse settings, particularly in scenarios where traditional pruning strategies become inefficient.

翻译：反向k近邻（RkNN）查询是空间数据库、基于位置的分析以及推荐系统中的基础问题。现有最先进的技术依赖于R树及其变体支持的空间剪枝。然而，在设施数量少、用户分布密集或k值较大的挑战性场景中，这些剪枝方法的有效性会显著下降。为克服这些局限性，本文将在二维几何空间中的RkNN查询问题重新表述为图形光线投射问题，其中用户被建模为光线，设施被表示为几何基元。基于这一表述，我们开发了首个利用现代GPU上专用硬件光线追踪核心的算法与实现。这一新方法即使在k值较大、用户密度高以及设施分布极为稀疏的情况下，仍能保持强大的过滤性能。大量实验结果表明，所提方法在不同场景下均优于现有最先进的算法，尤其是在传统剪枝策略效率低下的情形中。