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最近邻（RkNN）查询是空间数据库、基于位置的分析和推荐系统中的基本查询。现有前沿技术依赖于R树及其变体的空间剪枝，但在设施数量少、用户分布密集或k值较大等具有挑战性的场景中，其剪枝效果显著下降。为克服这些局限，本研究将二维几何空间中的RkNN查询问题重构为图形光线投射问题，其中用户被建模为光线，设施表示为几何基元。基于这一重构，开发了首个利用现代GPU专用硬件光线追踪核心的算法与实现。该新方法即使在大k值、密集用户分布和高度稀疏设施分布的情况下，仍能保持强大的过滤性能。大量实验结果表明，所提方法在多种设置下优于现有算法，尤其在传统剪枝策略效率低下的场景中表现突出。