关键词: 轨迹数据流,滑动窗口,密度聚类,偏倚采样 中图法分类号TP301文献标识码A

Abstract: In management of the mobile object trajectory data stream in the field of mobile computing,the most commonly used technical means is sampling techniques,but the traditional uniform sampling is easy to lose some of the key changes in data,resulting in the phenomenon of loss of information．To solve this problem,we proposed a data stream based on the probability density clustering bias sampling algorithm．The algorithm in a sliding window model,makes full use of the distribution of characteristics of the the trajectory data stream itself,combines a bias sampling algorithm ideo-logy to overcome uniformly sampled data loss problems．Firstly the sliding window is divided into a strong cluster clustering techniques based on density data exists,weak clusters and excessive cluster,and then different sampling rates for different clusters biased sampling are given,thereby to obtain a final summary of the data stream．The experimental testing results of the set of actual data show that the algorithm ensures the sampling quality and has faster data processing capability.

Key words: Trajectory data stream,Sliding window,Density clustering,Bias sampling