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首页> 外文期刊>Journal of Intelligent Information Systems >Visually exploring movement data via similarity-based analysis
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Visually exploring movement data via similarity-based analysis

机译：通过基于相似度的分析直观地浏览运动数据

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摘要

Data analysis and knowledge discovery over moving object databases discovers behavioral patterns of moving objects that can be exploited in applications like traffic management and location-based services. Similarity search over trajectories is imperative for supporting such tasks. Related works in the field, mainly inspired from the time-series domain, employ generic similarity metrics that ignore the peculiarity and complexity of the trajectory data type. Aiming at providing a powerful toolkit for analysts, in this paper we propose a framework that provides several trajectory similarity measures, based on primitive (space and time) as well as on derived parameters of trajectories (speed, acceleration, and direction), which quantify the distance between two trajectories and can be exploited for trajectory data mining, including clustering and classification. We evaluate the proposed similarity measures through an extensive experimental study over synthetic (for measuring efficiency) and real (for assessing effectiveness) trajectory datasets. In particular, the latter could serve as an iterative, combinational knowledge discovery methodology enhanced with visual analytics that provides analysts with a powerful tool for "hands-on" analysis for trajectory data.
机译：通过移动对象数据库进行数据分析和知识发现，可以发现可在流量管理和基于位置的服务等应用程序中利用的移动对象的行为模式。为了支持此类任务，必须在轨迹上进行相似性搜索。该领域的相关工作主要受时间序列领域的启发，采用通用的相似性度量标准，而忽略了轨迹数据类型的特殊性和复杂性。为了为分析人员提供一个强大的工具包，本文提出了一个框架，该框架基于原始（空间和时间）以及轨迹的派生参数（速度，加速度和方向）提供了几种轨迹相似性度量，这些度量可以量化两条轨迹之间的距离，可用于轨迹数据挖掘，包括聚类和分类。我们通过对合成（用于测量效率）和真实（用于评估有效性）轨迹数据集的广泛实验研究来评估拟议的相​​似性度量。特别地，后者可以充当迭代的，组合的知识发现方法，并通过可视化分析加以增强，该方法为分析人员提供了用于“动手”分析轨迹数据的强大工具。

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