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. (1997) Data warehouse from architecture to implementation. Reading, MA, itd: Addison-Wesley
Berry, M.J.A., Linoff, G.S. (2000) Mastering data mining the art and science of customer relationship. New York, itd: Wiley
Bhavani, T. (1999) Data mining: Technologies, techniques, tools and trends
Birkes, D., Dodge, Y. (1993) Alternative methods of regression. New York, itd: Wiley
Breiman, L., Meisel, W.S. (1976) General estimates of the intrinsic variability of data in nonlinear regression models. Journal of the American Statistical Association, 71, 301-307
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984) Classification and regression trees. Belmont, CA: Wadsworth International Group
de Rosa, J.C., Viega, A., Medeiros, M.C. (2003) Tree-structured smooth transition regression models based on CART algorithm. Janeiro: Department of Economics
Denison, T., Mallick, B.K., Smith, A.F.M. (1998) A Bayesian CART algorithm. Biometrika, 85, 363-377
Gunderloy, M., Sneath, T. (2001) SQL Server Developer's Guide to OLAP with analysis services. Sybex
Jiawei, H., Kamber, M. (2001) Data mining: Concepts and techniques. San Francisco, CA, itd: Morgan Kaufmann
Krulj, D. (2003) Design and implementation of data warehouse systems. Belgrade: Faculty of Organizational Sciences, magistarski rad
Krulj, D., Suknović, M., Čupić, M., Martić, M., Vujnović, T. (2002) Design and development of OLAP system FOS student service. u: INFOFEST, Budva
Krulj, D., Vujnović, T., Suknović, M., Čupić, M., Martić, M. (2002) Algorithm of data mining: Good base for decision making. u: SYM-OP-IS, Tara
Lewis, P.A.W., Stevens, J.G. (1991) Nonlinear modeling of time series using multivariate adaptive regression splines, MARS. Journal of the American Statistical Association, 86, 864-877
Lory, O., Crandall, M. (2000) Programmers guide for Microsoft SQL server 2000. Redmond, WA: Microsoft Press
Narula, S.C., Wellington, J.F. (1982) The minimum sum of absolute errors regression: A state of the art survey. Internat. Statist. Rev., 50, 317-326
Seidman, C. (2001) Data mining with Microsoft SQL server 2000. Redmond, WA: Microsoft Press
Suknović, M., Čupić, M., Martić, M., Krulj, D. (2002) Design and development of FOS Data Warehouse. u: SYM-OP-IS, Tara
Suknović, M., Krulj, D., Čupić, M., Martić, M. (2002) Design and development of FOS Data Warehouse. u: SYMORG, Zlatibor
Vidette, P. (1996) Building a data warehouse for decision support. Prentice Hall
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