Similarity Search in Trajectory Databases
Similarity Search in Trajectory Databases
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DOI：
10.1109/time.2007.59
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发表时间：
2007-06
期刊：
影响因子：
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通讯作者：
N. Pelekis;Ioannis Kopanakis;Gerasimos Marketos;Eirini Ntoutsi;G. Andrienko;Y. Theodoridis
中科院分区：
文献类型：
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作者：
N. Pelekis;Ioannis Kopanakis;Gerasimos Marketos;Eirini Ntoutsi;G. Andrienko;Y. Theodoridis
弹道数据库(TD)管理是随着移动设备和定位技术的爆炸性发展而出现的一个相对较新的数据库研究课题。轨迹相似度搜索是TD中的一类重要查询，在轨迹数据分析和时空知识发现中有着广泛的应用。与传统的基于时间维度的相似性度量方法不同，本文提出了一种基于基元(空间和时间)和轨迹参数(速度和方向)的距离算子框架。该方法的新颖之处在于不仅提供了定性的不同方法来查询相似轨迹，而且支持轨迹聚类和分类挖掘任务，这无疑意味着一种量化两条轨迹之间距离的方法。对于每个提出的距离算子，我们设计了高度参数的算法，其效率通过使用合成和真实轨迹数据集的广泛的实验研究来评估。
Trajectory database (TD) management is a relatively new topic of database research, which has emerged due to the explosion of mobile devices and positioning technologies. Trajectory similarity search forms an important class of queries in TD with applications in trajectory data analysis and spatiotemporal knowledge discovery. In contrast to related works which make use of generic similarity metrics that virtually ignore the temporal dimension, in this paper we introduce a framework consisting of a set of distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). The novelty of the approach is not only to provide qualitatively different means to query for similar trajectories, but also to support trajectory clustering and classification mining tasks, which definitely imply a way to quantify the distance between two trajectories. For each of the proposed distance operators we devise highly parametric algorithms, the efficiency of which is evaluated through an extensive experimental study using synthetic and real trajectory datasets.