With continued development of location-based systems, large amounts of trajectories become available which record moving objects’ locations across time. If the trajectories collected by different location-based systems come from the same moving object, they are spliceable trajectories, which contribute to representing holistic behaviors of the moving object. In this paper, we consider how to efficiently identify spliceable trajectories. More specifically, we first formalize a spliced model to capture spliceable trajectories where their times are disjoint, and the distances between them are close. Next, to efficiently implement the model, we design three components: a disjoint time index, a directed acyclic graph of sub-trajectory location connections, and two splice algorithms. The disjoint time index saves a disjoint time set of each trajectory for querying disjoint time trajectories efficiently. The directed acyclic graph contributes to discovering groups of spliceable trajectories. Based on the identified groups, the splice algorithm findmaxCTR finds maximal groups containing all spliceable trajectories. Although the splice algorithm is efficient in some practical applications, its running time is exponential. Therefore, an approximate algorithm findApproxMaxCTR is proposed to find trajectories which can be spliced with each other with a certain probability within polynomial run time. Finally, experiments on two datasets demonstrate that the model and its components are effective and efficient.

中文翻译：

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轨迹拼接

随着基于位置的系统的不断发展，可以记录大量移动物体在整个时间的位置的轨迹。如果不同的基于位置的系统收集的轨迹来自相同的移动对象，则它们是可拼接的轨迹，这有助于表示运动对象的整体行为。在本文中，我们考虑如何有效地识别可拼接的轨迹。更具体地说，我们首先对一个拼接模型进行形式化，以捕获其时间不相交且它们之间的距离很近的可拼接轨迹。接下来，为了有效地实现该模型，我们设计了三个组件：不相交的时间索引，子轨迹位置连接的有向无环图以及两个拼接算法。不相交时间索引保存每个轨迹的不相交时间集，以有效查询不相交时间轨迹。有向无环图有助于发现可拼接轨迹组。根据识别出的组，拼接算法findmaxCTR查找包含所有可拼接轨迹的最大组。尽管拼接算法在某些实际应用中是有效的，但其运行时间却是指数级的。因此，提出了一种近似算法findApproxMaxCTR来找到在多项式运行时间内可以一定概率相互拼接的轨迹。最后，在两个数据集上的实验表明该模型及其组件是有效且高效的。