自适应参数的轨迹压缩算法

随着轨迹数据量的急剧增长，数据规模变得越来越庞大、复杂，给数据的存储、传输和分析带来了一系列的难题，迫切需要对移动对象的轨迹数据进行有效地压缩。现有的轨迹数据压缩算法大都是基于固定压缩阈值，来判定轨迹点是否被保留，在算法执行过程中，用户难以确定压缩阈值。针对现有轨迹数据压缩算法压缩阈值难以确定的缺点，本文提出了自适应参数的轨迹压缩算法。该算法根据用户期望达到的压缩效果，按照优先保证压缩比的策略，在保证压缩效率和压缩效果的情况下，帮助用户自动确定压缩阈值，从而避免了用户需要根据自己的经验，进行反复实验来得到理想压缩阈值的过程。实验结果表明，本文提出的算法都具有较高的压缩效率和较好的压缩效果。

Trajectory Compression Algorithm with Adaptive Parameter

As exponentially increasing in the amount of trajectory data, the scale of trajectory data is growing huge and complex. The size of this data makes it difficult to store, transmit and analyze. It is necessary to effectively compress these data. But, most of the existing trajectory data compression algorithms determine whether the trajectory points will be preserved based on the fixed compression threshold. However, it is hard to confirm the perfect compression threshold while the algorithm running. In order to overcome this disadvantage, this paper present trajectory compression algorithms with adaptive parameters. This method, based on the compression effort that users expect and the strategy ensuring the compression ratio priority, can help users to automatically determine the compression threshold while guaranteeing the compression efficiency and effect. It will avoid users find the perfect compression threshold by their experience and repeated experiments. The experimental results show that the proposed algorithm has high compression efficiency and good compression effect.