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The problem of trajectory similarity has been recently attracted research interest considerably, due to its importance in diverse fields. In this paper, the authors study trajectory similarity by attacking the problem taking an information retrieval perspective. Trajectories are first decomposed by using a grid and each trajectory is mapped to a multidimensional space where Latent Semantic Analysis is applied. Distance measures like Euclidean distance or cosine distance are applied to process similarity queries (range queries, k-NN queries). Performance evaluation results, based on real-life data sets, show the simplicity and effectiveness of the proposed scheme.
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