With the dramatic countrywide development of mobile internet, it becomes very important to extract valuable information from mobile device log data and report the analysis result through visualization method to help application developers and distributors maximize monetization opportunity. Currently, most of mobile log data analysis work is based on single dimension statistics, e.g., app download rank, and user retention rates. In order to mine deep information hiding behind mobile device log data and summarizes user characteristics. A method is proposed for analyzing users' characteristics and computing users' profile. An app topic model is constructed based on mobile log data, user clusters are build according to app topics, and two visualization methods are designed to show user characteristics clusters. Furthermore, user clusters are combined with time information and geographical information to show user characteristics from additional dimensions. Finally, a mobile log data visualization analysis B/S system is implemented to demonstrate the validity of the method by a case study.%随着我国移动互联网的迅猛发展,如何从海量移动终端日志数据中提取出有效信息,并进行合理、清晰的可视化分析,为工业界等提供有价值的统计分析功能显得尤为重要.目前,对于移动终端日志数据的研究和分析多是基于对单一属性的统计结果分析,如应用下载排行、用户留存率等.为了进一步挖掘移动终端日志数据背后深层次的隐含信息,更加准确地概括出移动终端用户的特征,提出了一种基于移动应用程序日志数据的人群特征分析与画像计算方法,构造了基于移动应用程序数据的主题模型,并将移动设备用户按照与不同应用主题的相关度进行聚类,得到了具有不同特征的人群,从而提出了基于层次气泡图和Voronoi Treemap的可视化展现与分析方案.进一步将人群特征与时间信息、地理位置信息相结合,从多角度可视化展现人群特征.最后,根据该研究内容,实现了B/S架构的日志数据可视化分析原型系统,并通过案例分析验证了该方法的有效性.
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