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teaching:projh402 [2021/09/18 18:32] ezimanyi [Dynamic Time Warping for Trajectories] |
teaching:projh402 [2021/09/18 18:40] ezimanyi [Symbolic trajectories] |
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| - | * {{:teaching:symbolic_trajectories.pdf|}} | + | * R.H. Guting, F Valdés, M.L. Damiani, {{:teaching:symbolic_trajectories.pdf|Symbolic Trajectories}}, ACM Transactions on Spatial Algorithms Systems, (1)2: 7, 2015 |
| ===== Trajectory Data Warehouses ===== | ===== Trajectory Data Warehouses ===== | ||
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| * Toni Giorgino, [[https://www.jstatsoft.org/article/view/v031i07|Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package]] | * Toni Giorgino, [[https://www.jstatsoft.org/article/view/v031i07|Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package]] | ||
| - | * S. Salvador, P. Chan [[https://cs.fit.edu/~pkc/papers/tdm04.pdf|FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space]] | + | * S. Salvador, P. Chan, [[https://cs.fit.edu/~pkc/papers/tdm04.pdf|FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space]] |
| ===== Geospatial Trajectory Similarity Measure ===== | ===== Geospatial Trajectory Similarity Measure ===== | ||
| One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. This is desirable for similarity-based retrieval, classification, clustering and other querying and mining tasks over moving objects' data. The existing movement similarity measures can be classified into two classes: (1) spatial similarity that focuses on finding trajectories with similar geometric shapes, ignoring the temporal dimension; and (2) spatio-temporal similarity that takes into account both the spatial and the temporal dimensions of movement data. | One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. This is desirable for similarity-based retrieval, classification, clustering and other querying and mining tasks over moving objects' data. The existing movement similarity measures can be classified into two classes: (1) spatial similarity that focuses on finding trajectories with similar geometric shapes, ignoring the temporal dimension; and (2) spatio-temporal similarity that takes into account both the spatial and the temporal dimensions of movement data. | ||