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teaching:projh402 [2021/09/08 09:09] ezimanyi [MobilityDB on Google Cloud Platform] |
teaching:projh402 [2021/09/18 18:38] ezimanyi [Symbolic trajectories] |
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| * [[https://github.com/MobilityDB/MobilityDB-AWS|MobilityDB-AWS]] | * [[https://github.com/MobilityDB/MobilityDB-AWS|MobilityDB-AWS]] | ||
| + | **Status**: taken | ||
| ===== Implementing TSBS on MobilityDB ===== | ===== Implementing TSBS on MobilityDB ===== | ||
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| Links: | Links: | ||
| - | * {{:teaching:symbolic_trajectories.pdf|}} | + | * R.H. Guting, F Valdés, M.L. Damiani, {{:teaching:symbolic_trajectories.pdf|Symbolic Trajectories}}, ACM Trans. Spatial Algorithms Syst., Vol. 1, No. 2, Article 7 (July 2015) |
| ===== Trajectory Data Warehouses ===== | ===== Trajectory Data Warehouses ===== | ||
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| The goal of this project is to survey the state of the art in geospatial trajectory data cleaning, both model-based and machine learning. The work also includes prototyping and empirically evaluating a selection of these methods in the MobilityDB system, and on different real datasets. These outcomes should serve as a base for a thesis project to enhance geospatial trajectory data cleaning. | The goal of this project is to survey the state of the art in geospatial trajectory data cleaning, both model-based and machine learning. The work also includes prototyping and empirically evaluating a selection of these methods in the MobilityDB system, and on different real datasets. These outcomes should serve as a base for a thesis project to enhance geospatial trajectory data cleaning. | ||
| + | ===== Dynamic Time Warping for Trajectories ===== | ||
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| + | The dynamic time warping (DTW) algorithm is able to find the optimal alignment between two time series. It is often used to determine time series similarity, classification, and to find corresponding regions between two time series. Several dynamic time warping implementations are available. However, DTW has a quadratic time and space complexity that limits its use to small time series data sets. Therefore, a fast approximation of DTW have been proposed that has linear time and space complexity. | ||
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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. | ||
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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]] | ||
| + | * 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. | ||