Application Of Data Warehouse Technology In College Enrollment And Employment Decision-Making

With the development of science and technology,society has entered the era of big data,people pay more and more attention to the value of information,and the importance of data warehouse has reached an unprecedented height.Data warehousing is based on the technology of the database,and it is increasingly developed in the current large-scale,multi-source,heterogeneous,and dynamic massive data.At present,the employment situation is becoming more and more serious.The colleges and universities across the country attach great importance to the issue of enrollment and employment.What are the students' intentions to choose colleges and universities,and which professional students have high employment rates in all kinds of work departments are the focus of our attention.This thesis puts forward a series of questions on how to better guide the enrollment employment management and decision-making,and uses OLAP and data mining techniques to carry out analysis and research on the paper data.Fitst of all,in order to better and more rationally design the data warehouse,this paper uses three design models: physical model,conceptual model and logical model.At the same time,in order to ensure the smooth establishment of the data warehouse,this paper has dealt with the data sources of the two major systems of enrollment and employment,and has carried out the work of drawing,converting,cleaning and loading the enrollment and employment data.Secondly,based on the familiarity with the business process of enrollment and employment,this paper obtains more convincing for the classification and OLAP processing for the admission situation,the quality of students,the number of students in each major,and the data warehouse of enrollment and employment for all ages.The result of force.Thirdly,in order to explore the intrinsic connection of each data in the data warehouse of enrollment and employment,this paper uses three data mining algorithms,including association analysis,decision tree analysis and cluster analysis to conduct data mining,mainly for attribute value vacancy in decision tree analysis.Simplified optimization and improvement of the entropy algorithm.The analysis shows that there is a correlation between the correspondence site,the year of admission,the level of education,the grade of achievement,the degree of education,and the registration rate.Statistical analysis of the results of the above OLAP processing and data mining,and finally put forward recommendations and strategies for college admissions employment decision-making.The research in this paper applies data warehouse technology in the field of enrollment and employment,and discovers various potential and valuable rules from many historical data,so as to further scientifically guide employment and carry out enrollment promotion efficiently,which will help improve college enrollment.The quality and quality of graduate employment have important practical significance for the development of the entire university and the improvement of the quality of students.