During the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought i...
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During the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought in other domains. In this work, we focus on data compression techniques with the intention to minimize the size of trajectory data, while, at the same time, minimizing the impact on the trajectory analysis methods. To this extent, we evaluate five lossy compression algorithms: Douglas-Peucker (DP), Time Ratio (TR), Speed Based (SP), Time Ratio Speed Based (TR_SP) and Speed Based Time Ratio (SP_TR). The comparison is performed using four distinct real world datasets against six different dynamically assigned thresholds. The effectiveness of the compression is evaluated using classification techniques and similarity measures. The results showed that there is a trade-off between the compression rate and the achieved quality. The is no "best algorithm" for every case and the choice of the proper compression algorithm is an application-dependent process.
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 compression algorithm 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.
The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to red...
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The amount of spatiotemporal data collected by gadgets is rapidly growing, resulting in increasing costs to transfer, process and store it. In an attempt to minimize these costs several algorithms were proposed to reduce the trajectory size. However, to choose the right algorithm depends on a careful analysis of the application scenario. Therefore, this paper evaluates seven general purpose lossy compression algorithms in terms of structural aspects and performance characteristics, regarding four transportation modes: Bike, Bus, Car and Walk. The lossy compression algorithms evaluated are: Douglas-Peucker (DP), Opening-Window (OW), Dead-Reckoning (DR), Top-Down Time-Ratio (TS), Opening-Window Time-Ratio (OS), STTrace (ST) and SQUISH (SQ). Pareto Efficiency analysis pointed out that there is no best algorithm for all assessed characteristics, but rather DP applied less error and kept length better-preserved, OW kept speed better-preserved, ST kept acceleration better-preserved and DR spent less execution time. Another important finding is that algorithms that use metrics that do not keep time information have performed quite well even with characteristics time-dependent like speed and acceleration. Finally, it is possible to see that DR had the most suitable performance in general, being among the three best algorithms in four of the five assessed performance characteristics.
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