Semantic periodic pattern mining from spatio-temporal trajectories

Rapid development in GPS tracking techniques produces a large number of spatio-temporal trajectory data. The analysis of these data provides us with a new opportunity to discover behavioural patterns. Spatio-temporal periodic pattern mining is finding temporal regularities for interesting places. Mining periodic patterns from spatio-temporal trajectories reveals useful and important information about people’s regular and recurrent movements and behaviours. Existing periodic pattern mining algorithms suffer from two main drawbacks. They assume regularly sampled and evenly spaced trajectory data as input which is unlike real world data, traditional methods also fail to consider background aspatial information despite many applications requiring a semantic interpretation of movement behaviours. In this paper, we propose a new semantic periodic pattern mining algorithm from spatio-temporal trajectories that overcomes these two drawbacks from past studies. Experimental results with real world datasets demonstrate the efficiency and effectiveness of our proposed method.

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