Design And Implementation Of The Epidemic Monitoring System Based On Data Warehouse

Recently, with the continuous development of data warehouse technology and decision support systems applications in the industry, the new decision support system model that based on data warehouse and online analytical processing technology is also gradually developed in the industry. Using the data warehouse, decision-makers could organize, understand and use data systematically for decision-making. With the development of IT, the networking,informatization,standardization of epidemic monitoring and the unionization of the Epidemic Prevention and Control System could effectively prevent the epidemic to ensure strict control of rabies in guiding animal immunization, control of major animal epidemic, and promote public health security and other aspects of great significance.This paper based on the the goal that establish the "Canine Madness Information Monitoring Management System" which could be used by departments as public health and animal husbandry, etc. In order to realize the report and query to the epidemic of Animal rabies and offer support means for decision- making for all Grades. Through the network, we could finish the job of reporting and collecting the rabies epidemic information, also could be provide the function of inquiry,classification,analysis and statistics. In addition, we can assign and manage these by multilevel permissions.This paper mainly described the Business Needs Analysis,the Architecture of System,Key Supporting Technologies and System Architecture, also has done some research and implementation of the overall system.In view of the present of this epidemic monitoring platform, make use of the data warehouse and online analytical processing technology and decision support theory to design and implement the Canine Madness Information Monitoring and Decision Support System. The platform with the need of the National Science and Technology Support Program, combined with the disease data, the platform integrated the Struts 2.0,Hibernate,Spring,Quiee Report and other technologies, building the data warehouse firstly, and characterization and classification of the mass and irregular epidemic data in the system and to store these data in the data warehouse, and become the subject of decision-useful data. Use the OLAP to handle these data for multi-angle analysis and demand forecasts, and show the statistical information to the decision analysts by real-time statistical reports or charts to accelerate the epidemic warning response process.