International Journal of Education and Management Engineering(IJEME)
ISSN: 2305-3623 (Print), ISSN: 2305-8463 (Online)
Published By: MECS Press
IJEME Vol.2, No.10, Oct. 2012
A MDA-based campus data analysis and visualization framework
Full Text (PDF, 250KB), PP.65-71
As the rapid development of high education informatization, it will be a new important research topic to con-duct the data mining and visual analytics to the existing data in the separated information systems. The existing campus information system is the integration of some business systems, so the system has some problems, such as it stores the data separately and it has poor ability to analyze data. In order to solve these problems, we pro-pose Model Driven Architecture (MDA) based campus data analysis and visualization framework. The frame-work is composed of multi-dimension data modeling, data extraction, visualization-based data exhibition, and some other modules. Data extraction solves the problem caused by separately stored data and heterogeneous data. Multi-dimension data modeling analysis and visualization enhances the analysis ability of existing system. Based on the idea of MDA modeling analysis, we provide a rapid develop platform of campus business for both business analyzers and developers.
Cite This Paper
Jiangning Xie,Xueqing Li,Lei Wang,Yuzhen Niu,"A MDA-based campus data analysis and visualization framework", IJEME, vol.2, no.10, pp.65-71, 2012.
 G. Bastien, Sen Wu, Xuedong Gao. ”Data warehouseing and data mining”, China Machine Press, 2001, pp. 55-100.
 C. Y. Ren, W. Q. Luo,. “The Combination and Application of CRM and DM in Campus Information System”[J]. Computer Engineering and Applications, 2003. (In Chinese)
 J. Li, “Data Mining and Its Application in Campus Teaching Informatization Management” [J]. Colleges and Universities Science Research, 2007. (In Chinese)
 Model Driven Architecture , http://www.omg.org/mda/index.html.
 Heer, J., S. K. Card, J. A. Landay. prefuse: A Toolkit for Interactive Information Visualization. ACM Human Factors in Computing Systems (CHI), 2005.
 SUN Yang, FENG Xiaosheng. ”Survey on the Research of Multidimensional and Multivariate Data Visualization”, Computer science, 2008, pp.1-2.
 Y.-H. Fua, M. O. Ward, and E. A. Rundensteiner. Hierarchical parallel coordinates for exploration of large datasets. In Proc. of IEEE Visualization, pages 43–50, 1999.
 K. T. McDonnell and K. Mueller. Illustrative parallel coordinates. Eurographics/IEEE-VGTC Symposium on Visualization. Vol 27(3), 2008.
 Sven Bachthaler and Daniel Weiskopf. Continuous scatter plots. IEEE Trans. on Vis. and Comp. Graph., Vol.14(6), pages 1428-1435,2008.
 J. LeBlanc, M.Ward, and N.Wittels. Exploring n-dimensional databases. Proc. of Visualization ’90, p. 230-7, 1990.
 T. Zhang, R. Ramakrishnan, and M. Livny. Birch: an efficient data clustering method for very large databases. SIGMOD Record, vol.25(2), p. 103-14, June 1996.
 YANG Dongqing, MA Xiuli and TANG Shiwei. ”Business Modeling and Data Mining”, China Machine Press, 2005, pp.56-73.