The widespread use of position-tracking devices leads to vast volumes of spatial-temporal data aggregated in the form of the trajectory data streams. Extracting useful knowledge from moving object trajectories can benefit many applications, such as traffic monitoring, military surveillance, and weather forecasting. Most of the knowledge gleaned from the trajectory data illustrates different kinds of group patterns, i.e., objects that travel together for some time. In the real world, the trajectory of the moving objects can change with time. Thus, existing approaches can miss a new pattern because they have a stringent requirement for moving object participators in a group movement pattern. To address this issue, we introduced a new type of moving object group pattern called an evolving companion. It allows some members of the group to leave and join anytime if some participators stay connected for all time intervals. In this pattern discovery, we model an incremental discovery solution to retrieve the evolving companion efficiently over the data stream. We evaluated the efficiency and effectiveness of our approach on two real vehicles and one synthetic dataset. Our method performed well compared with existing pattern discovery methods; for example, it was about 50% faster than Tang et al.'s buddy-based clustering method.
The general usability of location tracking devices has been generated a high volume of spatial-temporal data in the form of trajectory. Exploring useful knowledge from these trajectory data can contribute to understanding many real-world applications, such as traffic monitoring and weather forecasting. The main task of trajectory data analysis is the tracking of an object group movement pattern. Existing algorithms, studying the evolving structure of moving object trajectories, have high computational complexity, particularly when tracking loose group companions. To address this problem, we describe a loose group companion tracking framework over trajectory data streams in an incremental manner, which reduces computational time. Loose group companion is the moving objects group that travels together. However, some members are allowed to leave at some timestamp. A crucial part of our framework is the micro-group based loose group companion discovery. It follows a moving object group and then incrementally detects the loose group companions. We validated our techniques using two real vehicle data sets and one synthetic data set. Our approach was, on average, 45% faster than previous algorithms.INDEX TERMS Group pattern, loose group companion, trajectory data stream, moving object clustering, spatial-temporal pattern.
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