The recent increase in the use of GPS-enabled devices has introduced a new demand for efficiently storing trajectory data. In this paper, we present a new technique that has a higher compression ratio for trajectory data than existing solutions. This technique splits trajectories into sub-trajectories according to the similarities among them. For each collection of similar sub-trajectories, our technique stores only one subtrajectory's spatial data. Each sub-trajectory is then expressed as a mapping between itself and a previous sub-trajectory. In general, these mappings can be highly compressed due to a strong correlation between the time values of trajectories. This paper presents evaluation results that show the superiority of our technique over previous solutions.
Trajectory compression algorithms enable efficient transmission, storage, and processing of trajectory data by eliminating redundant information. While a large number of compression algorithms have been developed, there is no comprehensive and convenient benchmarking system for evaluating these algorithms. We will demonstrate TrajMetrix, our system that meets the above need. We will show how TrajMetrix can be used to gain insights into the benefits and drawbacks of various compression algorithms given different compression requirements.From the knowledge attained by using TrajMetrix, we developed SQUISH-E (Spatial QUalIty Simplification Heuristic -Extended). This algorithm uses a priority queue to preferentially remove points based on the error introduced by their removal. Through live demonstrations that use both synthetic and real data sets, we will show the ability of SQUISH-E to effectively bound compression error with low computational overhead.
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