移动计算环境下远程用户体验数据挖掘方法研究

在移动计算环境下,通过对远程用户的体验数据优化挖掘,满足远程用户的个性化需求,提高对远程用户QoS服务质量。传统的数据挖掘方法采用显著特征关联信息提取算法,当远程用户体验数据之间的差异性特征不明显时,挖掘的准确性不好。提出一种基于关联用户自适应链路跟踪补偿的移动计算环境下远程用户体验数据挖掘模型,进行远程用户体验数据挖掘模型的总体设计和数据结构特征分析,对采集的远程用户体验数据进行非线性时间序列分解,对数据序列通过自相关特征匹配和特征压缩实现挖掘数据的指向性信息优化提取,采用关联用户自适应链路跟踪补偿方法实现对数据挖掘误差的控制和补偿,提高了数据挖掘的准确性和有效性。仿真结果表明,采用该挖掘方法进行移动计算环境下远程用户体验数据挖掘的准确度高,实时性较好,满足了移动远程用户的个性化需求,提高了对用户服务的针对性。

Research on remote user experience data mining method in mobile computing environment

in the mobile computing environment, through the optimization of the remote user experience data mining, to meet the personalized needs of remote users, improve the quality of QoS service for remote users. The traditional data mining methods use significant feature association information extraction algorithm, when the difference between the remote user experience data is not obvious, the accuracy of mining is not good. Put forward a associated with the user adaptive link tracking compensation of mobile computing environment remote user experience data mining model based on, remote user experience data mining model of overall design and data structure feature analysis, on the acquisition of the remote user experience data of non linear time series decomposition, the sequence of data by self correlation feature matching and feature compression to achieve data mining point of information optimization extraction, associated user adaptive link tracking compensation method to realize the error control and compensation of data mining is used to improve the accuracy and efficiency of the data mining. Simulation results show that using the mining method for mobile computing environment remote user experience data mining of high accuracy, real-time well and meet the personalized needs of remote mobile users, the increase of user services targeted.