Recent research has focused on Continuous K-Nearest Neighbor (CKNN) query over moving objects in road networks. A CKNN query is to find among all moving objects the K-Nearest Neighbors (KNNs) of a moving query point within a given time interval. As the data objects move frequently and arbitrarily in road networks, the frequent updates of object locations make it complicated to process CKNN accurately and efficiently. In this paper, according to the relative moving situation between the moving objects and the query point, a Moving State of Object (MSO) model is presented to indicate the relative moving state of the object to the query point. With the help of this model, we propose a novel Object Candidate Processing (OCP) algorithm to highly reduce the repetitive query cost with pruning phase and refining phase. In the pruning phase, the data objects which cannot be the KNN query results are excluded within the given time interval. In the refining phase, the time subintervals of the given time interval are determined where the certain KNN query results are obtained. Comprehensive experiments are conducted and the results verify the effectiveness of the proposed methods.

中文翻译：

---

基于路网中移动物体的速度和方向的连续K最近邻处理。

最近的研究集中在对道路网络中移动物体的连续K最近邻（CKNN）查询。CKNN查询是在给定的时间间隔内，在所有运动对象中找到运动查询点的K最近邻（KNN）。随着数据对象在道路网络中频繁频繁地移动，对象位置的频繁更新使准确有效地处理CKNN变得很复杂。本文根据运动物体与查询点之间的相对运动情况，提出了一种运动物体状态模型（MSO），以指示物体相对于查询点的相对运动状态。在该模型的帮助下，我们提出了一种新颖的对象候选处理（OCP）算法，可以在修剪阶段和精炼阶段极大地减少重复查询的成本。在修剪阶段，在给定的时间间隔内，将排除不能作为KNN查询结果的数据对象。在优化阶段，确定给定时间间隔的时间子间隔，以获取某些KNN查询结果。进行了全面的实验，结果验证了所提方法的有效性。