Research And Implementation Of Low-code Personnel Data Analysis Platform For Complex Conditions

Low-code development utilizes visual drag-and-drop tools to reduce the development threshold and make it easier to develop business applications.In human resource management,the complexity and diversity of data present chalenges for data analysis,and low-code development can improve the efficiency of application development and further enhance the level of human resource management.A low-code personnel big data analytics platform for complex conditions based on streaming programming and variability techniques is designed and implemented in this thesis.The platform includes highly available visual data warehouse data flow model construction and data analysis model building blocks,and supports multi-heterogeneous data source access,data cleaning,transformation,and complex data analysis modeling.The research and development process of the platform,including model-driven development,streaming programming,and software variability modeling,is described.An actual employee portrait application case is used to demonstrate the platform’s lowcode personnel big data analysis operation using unstructured evaluation document data and structured evaluation systems constructed by personnel experts as input sources.The results show that the platform designed and implemented in this thesis can effectively handle large-scale personnel data,help enterprises conduct data analysis more intelligently and refine their human resources management,and provide a new solution for data analysis in the field of human resource management.