Extraction and representation of the events in trajectory data enable us go beyond the primitive and quantitative values and focus on the high level knowledge. On the other hand, it enables the applications of vast off the shelf methods, which was originally designed for mining event sequences, to trajectory data. In this paper, the problem of symbolizing trajectory data is addressed. We first introduce a static Symbolization method, in which typical sub-trajectories are generated automatically based on the data. For facilitating the data mining process on streaming trajectories, we also present an incremental method, which dynamically adjusts the typical sub-trajectories according to the most recent data characters. The performances of our approaches were evaluated on both real data and synthetic data. Experimental results justify the effectiveness of the proposed methods and the superiority of the incremental approach.
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