Today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to d...
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Today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compressionalgorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.
Background: Mass movement trajectory data with real scenarios has been evolved with big data mining to solve the data redundancy problem. Methods: This paper proposes a parallel path based on the Map Reduce compressio...
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Background: Mass movement trajectory data with real scenarios has been evolved with big data mining to solve the data redundancy problem. Methods: This paper proposes a parallel path based on the Map Reduce compression method, using two kinds of piecewise point mutual crisscross, the classified method of trajectory, and then segment trajectory distribution to multiple nodes to parallelize the compression. Results: Finally, the results based on both compression methods have been simulated for the different real-time data by merging both techniques. Conclusion: The performance test results show that the parallel trajectorycompression method proposed in this paper can greatly improve the compression efficiency and completely eliminate the error caused by the failure of the correlation between the segments.
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