作者
Ticiana L Coelho Da Silva, Karine Zeitouni, José AF de Macêdo
发表日期
2016/6/13
研讨会论文
2016 17th IEEE International Conference on Mobile Data Management (MDM)
卷号
1
页码范围
112-121
出版商
IEEE
简介
Movement tracking becomes ubiquitous in many applications, which raises great interests in trajectory data analysis and mining. Most existing approaches cluster the whole trajectories offline. This allows characterizing the past movements of the objects but not current patterns. Recent approaches for online clustering of moving objects location are restricted to instantaneous positions. Subsequently, they fail to capture moving objects' behavior over time. By continuously tracking moving objects' sub-trajectories at each time window, rather than just the last position, it becomes possible to gain insight on the current behavior, and potentially detect mobility patterns in real time. In this work, we tackle the problem of discovering and maintaining the density based clusters in trajectory data streams, despite the fact that most moving objects change their position over time. We propose CUTiS, an incremental algorithm to …
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TLC Da Silva, K Zeitouni, JAF de Macêdo - 2016 17th IEEE International Conference on Mobile …, 2016