Research On Data Warehouse Technique Applied To Social Security Field

Social Security is a career connecting with national economy and people's livelihood. With the continuously development of Chinese Social Security, effective analysis of large amounts of accumulated historical data has become an urgent demand in the current Social Security industry, which offers correct direction for decision-making and elaborate evidence for management and execution. The appearance and gradual maturity of data warehouse and relevant techniques enable the above mentioned issues to be worked out. Data warehouse makes it possible that enormous data can be organized and stored in an efficient, comprehensive, accurate and orderly way; LAP and data mining can utilize efficacious analysis to find the rules hidden within data.Nowadays, the Labor and Social Security Bureau in Guiyang City has set up the Social Security Information Management System, covering more than 600,000 people, with the items of mainframe system data up to ten million, which creates a good occasion to establish data warehouse and apply OLAP and data mining to the system. Hence, in 2005, Guiyang Science and Technology Bureau proposed a project conducting the research and development on "Regional Social Economy Data Warehouse and OLAP System". This thesis is working as the foundation of this project.Based on keeping track of domestic and overseas advanced technology, the thesis conducts a systematic research on how to develop data warehouse system; and probes into its application to social security work, with consideration of current requirements in the industry. The methodic analysis on the following three techniques—data warehouse, OLAP, and data mining—paves the way for expatiating how to construct a decision-making sustain system for social security. To be specific, the thesis brings forward an overall plan to establish such a system in Guiyang in virtue of data warehouse and related techniques; discusses system implementation on SQL Server 2005 data warehouse platform; concentrates on subject confirmation of OLAP, construction of multi-dimension data models and mining models; investigates typical arithmetic of association rule and works over pertinent improvements with a view to the settlement of practical problems in the system.