| In recent years,mobile internet has become the most important direction of internet development.The mobile internet users have produced a lot of raw data when they are using the mobile applications.These raw data contains great values for research and analysis.It's necessary to analyze the various user behavior in the lifecycle of any mobile application.Using scientific analysis results on user behavior can provide powerful decision-making for product design.Because the traditional data collection and analysis method on PC is not applicable to mobile applications,in order to collect the original user interaction data from mobile terminals quickly in real time,this paper is doing in-depth research and implementation on data capture,data storage,data analysis and data display.The work of this paper can be divided into the following four aspects: Firstly,the methods of capturing the metadata in mobile and transmitting the data to the server are researched and implemented.Secondly,the methods of metadata analysis and modeling are researched and implemented,also a proper data index is proposed.Thirdly the web services and exception handling with NodeJS are researched and implemented.Finally,the data storage with MongoDB and extended big data solution with Hadoop are researched and implemented.At the end of the paper,a real case is introduced as an experimentation to verify the feasibility and effectiveness of the system.And the results of the platform analysis are displayed in the form of a chart to the web page,so that the application managers can get really useful value from the practice of mobile user behavior analysis. |
