Data warehousing and data mining: A case study
DOI:
https://doi.org/10.2298/YJOR0501125SKeywords:
decision support systems, data mining, data warehouse, MOLAP, regression trees, CARTAbstract
This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS), University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.References
Barry, D., Data Warehouse from Architecture to Implementation, Addison-Wesley, 1997.
Berry, M.J.A., and Linoff, G., "Mastering data mining", The Art and Science of Customer Relationship Management, 1999.
Bhavani, T., Data Mining: Technologies, Techniques, Tools and Trends, 1999.
Birkes, D., and Dodge, Y., Alternative Methods of Regression, John Wiley & Sons, 1993.
Breiman, L., and Meisel, W.S., “General estimates of the intrinsic variability of data in nonlinear regression models”, Journal of the American Statistical Association, 71 (1976) 301-307.
Breiman, L.J.H., Friedman, R.A.O., and Stone, C.J., Classification and Regression Trees, Belmont Wadsworth Int. Group, 1984.
De Rosa, J.C., Viega, A., and Medeiros, M.C., Tree-Structured Smooth Transition Regression Models Based on CART Algorithm, Department of Economics, Janeiro, 2003.
Denison, T., Mallick, B.K., and Smith, A.F.M., “A Bayesian CART algorithm”, Biometrika, 85 (1998) 363-377.
Gunderloy, M., and Sneath, T., SQL SERVER Developer’s Guide to OLAP with Analysis Services, Sybex, 2001.
Jiwei, H., and Micheline, K., Data Mining: Concepts and Techniques, Simon Fraser University, 2001.
Krulj, D., "Design and implementation of data warehouse systems", M Sc. Thesis, Faculty of Organizational Sciences, Belgrade, 2003.
Krulj, D., Suknović, M., Čupić, M., Martić, M., and Vujnović, T., "Design and development of OLAP system FOS student service", INFOFEST, Budva, 2002.
Krulj, D., Vujnović, T., Suknović, M., Čupić, M., and Martić, M., "Algorithm of Data Mining, good base for decision making", SYM-OP-IS, Tara, 2002.
Lewis, P.A.W., and Stevens, J.G., “Nonlinear modeling of time series using multivariate adaptive regression splines (MARS)”, Journal of the American Statistical Association, 86 (1991) 864-877.
Lory, O., and Crandall, M., Programmers Guide for Microsoft SQL Server 2000, Microsoft Press, 2001.
Narula, S.C., and Wellington, J.F., “The minimum sum of absolute errors regression: A state of the art survey”, Internat. Statist. Rev., 50 (1982) 317-326.
Seidman, C., Data Mining with Microsoft SQL Server 2000, Microsoft Press, 2001.
Suknović, M., Čupić, M., Martić, M., and Krulj, D., "Design and development of FOS Data Warehouse", SYM-OP-IS, Tara, 2002.
Suknović, M., Krulj, D., Čupić, M., and Martić, M., "Design and development of FOS Data Warehouse", SYMORG, Zlatibor, 2002.
Vidette, P., Building a Data Warehouse for Decision Support, Prentice Hall, 1996.
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