Computing with Spatial Trajectories
Computing with Spatial Trajectories
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DOI：
10.1007/978-1-4614-1629-6
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发表时间：
2011-10
期刊：
影响因子：
--
通讯作者：
Yu Zheng;Xiaofang Zhou
中科院分区：
文献类型：
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作者：
Yu Zheng;Xiaofang Zhou
随着可用的移动物体轨迹数据量的迅速增长，对轨迹分析技术的需求也在不断增加。这样的功能可能会使一系列应用领域和服务受益，包括交通、科学、体育、基于预测的社会服务等等。本章首先概述了轨迹模式，并对文献中的轨迹模式进行了分类。接下来，它检查相对运动模式，这是本章后续讨论的基本背景。相对模式允许在引用运动对象之间的运动属性关系的数据中识别模式规范。然后，本章研究了基于磁盘和基于密度的模式，它们解决了相对运动模式的一些局限性。本章还回顾了轨迹模式挖掘的索引结构和算法。
In step with the rapidly growing volumes of available moving-object trajectory data, there is also an increasing need for techniques that enable the analysis of trajectories. Such functionality may benefit a range of application area and services, including transportation, the sciences, sports, and prediction-based and social services, to name but a few. The chapter first provides an overview trajectory patterns and a categorization of trajectory patterns from the literature. Next, it examines relative motion patterns, which serve as fundamental background for the chapter's subsequent discussions. Relative patterns enable the specification of patterns to be identified in the data that refer to the relationships of motion attributes among moving objects. The chapter then studies disc-based and density-based patterns, which address some of the limitations of relative motion patterns. The chapter also reviews indexing structures and algorithms for trajectory pattern mining.