Swarm: Mining Relaxed Temporal Moving Object Clusters
Swarm: Mining Relaxed Temporal Moving Object Clusters
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DOI：
10.14778/1920841.1920934
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发表时间：
2010-09-01
影响因子：
2.5
通讯作者：
Kays, Roland
中科院分区：
文献类型：
--
作者：
Li, Zhenhui;Ding, Bolin;Kays, Roland
定位技术的最新改进使得大量移动对象数据广泛可用。其中一个重要的分析是对一起运动的运动物体进行归档。现有的方法在定义移动对象簇时有很强的约束，即它们要求移动对象在连续的时间戳内粘在一起。我们的主要观察是，在集群中的移动对象实际上可能暂时发散和聚集在某些时间戳。受此启发，我们提出了集群的概念，捕捉移动对象的任意形状的集群内的某些时间戳，可能是不连续的。我们的目标是剥除所有的判别群，即封闭群。虽然封闭群的搜索空间非常巨大，但我们设计了一种方法ObjectGrowth来有效地检索答案。在ObjectGrowth中，提出了两种有效的剪枝策略，大大减少了搜索空间，并开发了一种新的闭包检查规则，以报告关闭的飞行。通过对真实的数据和大量合成数据的实证研究，验证了该方法的有效性和有效性。
Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to filed the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps.Motivated by this, we propose the concept of swarm which captures the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly nonconsecutive. The goal of our paper is to rind all discriminative swarms, namely closed swarm. While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.