Querying Recurrent Convoys over Trajectory Data
Querying Recurrent Convoys over Trajectory Data
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DOI：
10.1145/3400730
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Munkh-Erdene Yadamjav;Z. Bao;Baihua Zheng;F. Choudhury;H. Samet
中科院分区：
文献类型：
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作者：
Munkh-Erdene Yadamjav;Z. Bao;Baihua Zheng;F. Choudhury;H. Samet
配备位置定位设备的移动物体不断产生大量的时空轨迹数据。关于轨迹流的一个有趣的发现是一组在特定时间段内一起行进的物体。我们观察到，现有的关于挖掘联动对象的研究没有考虑联动对象之间的重要相关性，即联动模式的重复出现。在这项研究中，我们提出了从流轨迹中寻找重复的共同移动模式的问题，使我们能够发现在给定时间段内重复的最近的共同移动模式。现实生活轨迹数据的实验结果验证了我们方法的效率和有效性。
Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. We observe that existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the co-moving pattern. In this study, we propose the problem of finding recurrent co-moving patterns from streaming trajectories, enabling us to discover recent co-moving patterns that are repeated within a given time period. Experimental results on real-life trajectory data verify the efficiency and effectiveness of our method.