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Contribution to extract meaningful patterns from semantic trajectories
Last modified: 2017-05-19
Abstract
Explosive growth in geospatial and temporal data emphasizes the need for
automated discovery of spatiotemporal knowledge, Different algorithms have been proposed in the last few years for discovering different types of behaviours in trajectory data, integrating trajectory sample points with geographical and contextual data before applying mining techniques can be more gainful for the application users. It contributes to produce significant knowledge about movements and provide applications with richer and more meaningful patterns. Trajectory Outliers are a sort of patterns that can be extracted from trajectories. We propose a new approach for trajectory outlier detection based on semantic data besides than geometric data.
automated discovery of spatiotemporal knowledge, Different algorithms have been proposed in the last few years for discovering different types of behaviours in trajectory data, integrating trajectory sample points with geographical and contextual data before applying mining techniques can be more gainful for the application users. It contributes to produce significant knowledge about movements and provide applications with richer and more meaningful patterns. Trajectory Outliers are a sort of patterns that can be extracted from trajectories. We propose a new approach for trajectory outlier detection based on semantic data besides than geometric data.