We study the problem of processing continuous k nearest neighbor (CkNN) queries over moving objects on road networks, which is an essential operation in a variety of applications. We are particularly concerned with scenarios where the object densities in different parts of the road network evolve over time as the objects move. Existing methods on CkNN query processing are ill-suited for such scenarios as they utilize index structures with fixed granularities and are thus unable to keep up with the evolving object densities. In this paper, we directly address this problem and propose an object density aware index structure called ODIN that is an elastic tree built on a hierarchical partitioning of the road network. It is equipped with the unique capability of dynamically folding/unfolding its nodes, thereby adapting to varying object densities. We further present the ODIN-KNN-Init and ODIN-KNN-Inc algorithms for the initial identification of the kNNs and the incremental update of query result as objects move. Thorough experiments on both real and synthetic datasets confirm the superiority of our proposal over several baseline methods.

翻译：我们研究了道路网络上移动对象的连续k近邻（CkNN）查询处理问题，这是众多应用场景中的基础操作。特别关注对象密度随运动过程在不同路段动态演变的场景。现有CkNN查询处理方法因采用固定粒度的索引结构，难以适应动态变化的对象密度。针对这一难题，本文提出了一种称为ODIN的密度感知索引结构——基于道路网络层次化分割构建的弹性树。该结构具备节点动态折叠/展开的独特能力，从而自适应地匹配对象密度变化。我们进一步提出了ODIN-KNN-Init和ODIN-KNN-Inc算法，分别用于初始k近邻识别与对象移动时查询结果的增量更新。基于真实数据集和合成数据集的充分实验表明，所提方法在多项基准方法中具有显著优越性。