Trajectory Pattern Mining
Trajectory Pattern Mining
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DOI：
10.1007/978-1-4614-1629-6_5
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发表时间：
2011
影响因子：
3.9
通讯作者：
Hoyoung Jeung;Man Lung Yiu;Christian S. Jensen
中科院分区：
文献类型：
--
作者：
Hoyoung Jeung;Man Lung Yiu;Christian S. Jensen
随着可用的移动物体轨迹数据量的快速增长，也越来越需要能够分析轨迹的技术。这样的功能可以有益于一系列应用领域和服务，包括运输、科学、体育以及基于预测的服务和社会服务，仅举几例。本章首先提供了一个概述的轨迹模式和轨迹模式的分类从文献。接下来，它检查了相对运动模式，这是本章随后讨论的基本背景。相对模式使得能够在涉及移动对象之间的运动属性的关系的数据中识别模式的规范。然后，本章研究了基于光盘和基于密度的模式，解决了一些相对运动模式的局限性。本章还回顾了索引结构和算法的轨迹模式挖掘。
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.