Monitoring System, Based On The Teaching Quality Of The Data Warehouse

Data Mining(DM) based in Data Warehouse is a rapidly emerging field relevant to artificial intelligence , machine learning and database system etc. However, along with the quick developments of Data Mining and Data Warehouse technology, the successful application research which integrate DM with DW and use them in the field of education, has not been enough. At present, the keystone in education domain is how to develop the data warehouse of teaching quality monitor system(TQMS), and mine some data cubes about teaching subject for rules.In this paper, we take Huaqiao University as an example aimed to do research on the technology about Online Analytical Processing(OLAP) and DM relevant to build TQMS data warehouse. At first, we introduce some concepts such as DW, DM, OLAP architecture, data cubes and etc. Then we build OLAP system involved with some subjects and data mining model based in OLAP.After discussing the architecture of Association Rules Mining System based in data cubes, we mainly analyze the algorithms included intra-dimension association rules mining algorithms and inter-dimension association rules mining algorithms. Then, we give an example in each algorithm description to show how the algorithm goes and describe an Incremental Updating Association Rules Mining algorithm. Moreover, we achieve the association rules mining of current data about teacher character by adapting the conventional algorithm based on the transaction databases such as Apriori.Finally, we bring forward a new method named grey relation with weigh using the grey system theory into TQMS to analyze the subjects, such as the quality of test paper and teaching, qualified people, and so on. Then, we can use the analysis result for judging data validity.