轨迹器:轨迹群动力学的交互分析与探索

Abdullah M. Sawas, Abdullah Abuolaim, M. Afifi, M. Papagelis
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引用次数: 9

摘要

由于大量的现代跟踪设备及其大量的关键应用,挖掘大规模轨迹数据流(运动物体)已经成为越来越多的研究兴趣。该领域的一个具有挑战性的任务是挖掘运动对象的组模式。组模式挖掘描述了一种特殊类型的轨迹挖掘,它需要有效地发现在一段时间内彼此接近的对象的轨迹。为此,我们介绍了一个在线系统,用于交互式分析和探索轨迹群动态随时间和空间的变化。我们描述了这个系统,并证明了它在发现行人轨迹上的群体模式方面的有效性。系统架构和方法是通用的,可用于执行任何领域特定轨迹的组分析。
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Trajectolizer: Interactive Analysis and Exploration of Trajectory Group Dynamics
Mining large-scale trajectory data streams (of moving objects) has been of ever increasing research interest due to an abundance of modern tracking devices and its large number of critical applications. A challenging task in this domain is that of mining group patterns of moving objects. Group pattern mining describes a special type of trajectory mining that requires to efficiently discover trajectories of objects that are found in close proximity to each other for a period of time. To this end, we introduce Trajectolizer, an online system for interactive analysis and exploration of trajectory group dynamics over time and space. We describe the system and demonstrate its effectiveness on discovering group patterns on trajectories of pedestrians. The system architecture and methods are general and can be used to perform group analysis of any domain-specific trajectories.
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