Abstract
In this paper, we propose a new approach to measuring the similarity among indoor moving-object trajectories. Particularly, we propose to measure indoor trajectory similarity based on spatial similarity and semantic pattern similarity. For spatial similarity, we propose to detect the critical points in trajectories and then use them to determine spatial similarity. This approach can lower the computational costs of similarity search. Moreover, it helps achieve a more effective measure of spatial similarity because it removes noisy points. For semantic pattern similarity, we propose to construct a hierarchical semantic pattern to capture the semantics of trajectories. This method makes it possible to capture the implicit semantic similarity among different semantic labels of locations, and enables more meaningful measures of semantic similarity among indoor trajectories. We conduct experiments on indoor trajectories, comparing our proposal with several popular methods. The results suggest that our proposal is effective and represents an improvement over existing methods.
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This work is supported by the National Science Foundation of China under the grant number 61379037.
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Jin, P., Cui, T., Wang, Q., Jensen, C.S. (2016). Effective Similarity Search on Indoor Moving-Object Trajectories. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_12
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