基于MapReduce的轨迹压缩并行化方法

带有全球定位系统（GPS）功能设备的增多，产生大量的时空轨迹数据，给数据的存储、传输和处理带来了沉重的负担。为了减轻这种负担，各种轨迹压缩方法也随之产生。提出了一种基于MapReduce的并行化轨迹压缩方法，针对并行化导致的分段点前后轨迹的相关性被破坏的问题，首先，采用两种分段点相互交错的划分方法划分轨迹；然后，将分段轨迹分配到多个节点上进行并行化压缩；最后，对压缩结果进行匹配合并。性能测试分析结果表明，所提出的并行化轨迹压缩方法能够大幅提高压缩效率，而且能完全消除因分段导致分段点前后相关性被破坏带来的误差。

Parallel trajectory compression method based on MapReduce

The massive spatiotemporal trajectory data is a heavy burden to store, transmit and process, which is caused by the increase Global Positioning System (GPS)-enable devices. In order to reduce the burden, many kinds of trajectory compression methods were generated. A parallel trajectory compression method based on MapReduce was proposed in this paper. In order to solve the destructive problem of correlation nearby segmentation points caused by the parallelization, in this method, the trajectory was divided by two segmentation methods in which the segmentation points were interleaving firstly. Then, the trajectory segments were assigned to different nodes for parallel compression. Lastly, the compression results were matched and merged. The performance test and analysis results show that the proposed method can not only increase the compression efficiency significantly, but also eliminate the error which is caused by the destructive problem of correlation.