Optimal Design And Application Of User Health Information Service Platform Based On Big Data Processing

Medical health issues have always been with our lives, and also by the attention of the world. With the development of all kinds of wisdom medical technology, medical industry has also gone into the information age. Wisdom medical based on IoT technology produces large data when offering us health services. However in our country, because of the large population and the imperfection of health information systems,traditional data storage systems and data processing method have been behind the time. How to create a perfect platform for user health services which is able to process big data efficiently has become an urgent problem. Above all, this thesis studies Hadoop technology and improves the parallel data mining algorithms and finally designs a user health information service platform.This thesis focuses the characteristics of health data and designs the framework of user health information service platform which combines Spring MVC and Spring for Hadoop. Users can easily get the services via web page due to the separation of view,business logic and back-end storage. Meanwhile, users can continue to improve and add new services by their needs. In the realization of the platform, Hadoop is introduced to the business logic layer innovatively. Data access layer offers the interface of HBase. Data layer combines both relational database and HBase. Users can get Hadoop data processing services by the website. In the particular big data services, the thesis uses improved Apriori algorithm based on MapReduce to realize association data mining. It proved the feasibility and effectiveness of using Hadoop platform and data mining to deal with health data.This thesis concludes that the platform can be improved and big data service can be expanded. It looks into the feature of the implement and believes user health information service platform will make a contribution to health big data.