Application Of Business Intelligence In Data Analysis And Preference Selection Of National College Entrance Examinationand Admission

National College Entrance Examination (NCEE), a significant process in Chinese education system, is a connecting link between the preceding and the following, and furthermore plays an important role in the talent selection process for higher education. College application affects the prospect as well as the migration of the examinees that will grow into talent, in the whole society. For a long time, the massive data accumulated in the process of enrollment in college entrance examination has not been analyzed and utilized fully, and the examinees have problems in mastering sufficient information and information channels, which let the examinees make decisions blindly and venturesomely. Therefore, it’s really necessary to introduce a scientific method of data analysis for NCEE, and provides a scientific guidance and assistance for examinees’preference selection.One province in China has collected massive admission data over the years according to the online admissions, which includes useful information to the Provinces and Cities Admission Offices (PCAO), the colleges and the examinees. But it’s hard to make full use of the information because there is not enough data analysis techniques or tools and also because the complicacy of the data. It failed to provide support to the PCAO, and failed to provide support to examinees to make decisions too. So this article mainly focuses on the application of business intelligence technology in the analysis of data from the National College Entrance Examination and Admission (NCEEA), and it also focuses on the guide system for preference selection. This article first studies the application of data preprocessing technology, data warehouse technology, On-Line Analytical Processing(OLAP) technology, and data mining technology in data analysis of NCEEA. Then a guide system for preference selection of NCEE will be built based on the analysis.Around this issue, our team studied the admission data of the province over ten years by using these data analysis technology and successfully built a data warehouse. This article does further research base on previous work, and has eventually built an online simulation preference selection system which is instructive to the examinees.This article includes the following topics:(1)Historical admission data loading and cube buildingAfter studying and understanding the existing data warehouse, we have loaded the data of NCEEA from2009by using the ETL tool and built a unified cube. It’s the foundation of the OLAP analysis and data mining.(2)The implementation and show of OLAP analysisBase on the cube, we got some useful information from data analyzing on several topics using OLAP technology. These topics include the preference selection, the college’s plan, the examinee admission, and so on. And the analysis result is showed with the reports by using the SSRS and Web technology.(3)Study and implementation of data miningThis article use the SSAS(SQL Server Reporting Services) to study the way of applying data mining technology to the data analysis of NCEEA. Focus on topics such as preference selection, examinees’ admission, and so on. Study the application of decision tree and association rules. Evaluate the mining model and design the Front display solutions for data mining.(4)Study and implementation of online simulation Preference selection systemWith the achievements above and the SSH(Struts, Spring, Hibernate) framework, this article has designed an online simulation preference selection system. This system which is integrated with the data mining system can provide suggestions to the examinees in their preference selection. It’s of great practicality and scientificness and still lays foundations for successor work.