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teaching:projh402 [2021/09/18 18:28] ezimanyi [Dynamic Time Warping for Trajectories] |
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| The goal of this project is to survey and to prototype in MobilityDB the state of art methods in dynamic time warping. | The goal of this project is to survey and to prototype in MobilityDB the state of art methods in dynamic time warping. | ||
| - | + | * Toni Giorgino, [[https://www.jstatsoft.org/article/view/v031i07|Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package] | |
| - | * A. Vaisman and E. Zimányi. [[https://www.mdpi.com/2220-9964/8/4/170|Mobility data warehouses]]. ISPRS International Journal of GeoInformation, 8(4), 2019. | + | * S. Salvador, P. Chan [[https://cs.fit.edu/~pkc/papers/tdm04.pdf|FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space]] |
| - | * [[https://www.dma.dk/SikkerhedTilSoes/Sejladsinformation/AIS/Sider/default.aspx|Danish Maritine Authority]] | + | |
| - | * [[https://github.com/MobilityDB/MobilityDB-workshop|MobilityDB Workshop]] | + | |
| ===== 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. | ||