Continuous K-nearest neighbor search for moving objects

Y. Li, Jiong Yang, Jiawei Han
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引用次数: 30

Abstract

The paper describes a new method of continuously monitoring the k nearest neighbors of a given object in the mobile environment. Instead of monitoring all k nearest neighbors, we choose to monitor the k-th (nearest) neighbor since the necessary condition of changes in the KNN is the change of the k-th neighbor. In addition, rather than in the original space, we consider the moving objects in a transformed time-distance (TD) space, where each object is represented by a curve. A beach-line algorithm is developed to monitor the change of the k-th neighbor, which enables us to maintain the KNN incrementally. An extensive empirical study shows that the beach-line algorithm outperforms the most efficient existing algorithm by a wide margin, especially when k or n (the total number of objects) is large.
移动对象的连续k近邻搜索
本文描述了一种在移动环境中连续监测给定目标的k个近邻的新方法。我们选择监视第k(最近)邻居,而不是监视所有k个最近邻居,因为KNN变化的必要条件是第k个邻居的变化。此外,我们不再在原始空间中考虑运动物体,而是在变换的时距(TD)空间中考虑运动物体,其中每个物体都用曲线表示。开发了一种滩线算法来监测第k个邻居的变化,使我们能够增量地保持KNN。一项广泛的实证研究表明,海滩线算法比现有最有效的算法性能要好得多,特别是当k或n(对象总数)很大时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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