Modelling a Structure of a Fuzzy Data Warehouse
Applied Engineering
Volume 1, Issue 2, December 2017, Pages: 48-56
Received: Apr. 12, 2017; Accepted: Apr. 22, 2017; Published: Jun. 28, 2017
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Authors
Alain Kuyunsa Mayu, Faculty of Sciences, Regional Center for Doctoral Education in Mathematics and Computer Sciences, University of Kinshasa, Kinshasa, D. R. Congo
Nathanael Kasoro Mulenda, Faculty of Sciences, Department of Mathematics and Computer Sciences, University of Kinshasa, Kinshasa, D. R. Congo
Rostin Mabela Matendo, Faculty of Sciences, Department of Mathematics and Computer Sciences, University of Kinshasa, Kinshasa, D. R. Congo
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Abstract
In this article, we represent the structure of a fuzzy data warehouse. The elements of classification to build the fuzzy data warehouse are presented through the three following tasks: identification of the target-attribute, identification of linguistic terms and definition of membership functions. From these tasks, we present an approach of a fuzzy data warehouse modelling. This allows us to integrate fuzzy logic without affecting the data warehouse base.
Keywords
Target Attribute, Class Membership Attribute, Membership Degree, Membership Degree Attribute,Fuzzy Classification Table, Fuzzy Membership Table
To cite this article
Alain Kuyunsa Mayu, Nathanael Kasoro Mulenda, Rostin Mabela Matendo, Modelling a Structure of a Fuzzy Data Warehouse, Applied Engineering. Vol. 1, No. 2, 2017, pp. 48-56. doi: 10.11648/j.ae.20170102.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Zadeh, L. A. (1973). The Concept of a Linguistic Variable and its Application to Approximate Reasonning-1, Informations Sciences Vol.8/3, p.199-249.
[2]
Dubois, D., Prade, H.(1985). Théories des possibilités, Applications à la représentation des connaissances en informatique, Masson, 2e édition. Academic Press, NewYork.
[3]
Zadeh, L. A. (1978). Fuzzy Sets as a Basis for a Theory of Possibility, Fuzzy Sets and Systems Vol.1, 3-28.
[4]
Buckley, J. J. (1989). Solving Possibilistic Linear Programming Problems, Fuzzy Sets and Systems Vol. 31, 329-341.
[5]
Kaufmann (1977). Introductionàl a théorie des sous-ensembles flous, A l'usage des Ingénieurs, (Fuzzy Sets Theory), Tome 1, Éléments théoriques de base, Masson, Paris.
[6]
Klir, G. J., Folger, T. A. (1988). Fuzzy Sets, Uncertainty, and Information, Prentice-Hall, Englewood Cliffs, NJ.
[7]
Buckley, J. J. (1988a). Possibility and Necessity in Optimization, Furzy Sets and Systems Vol.25, 1-13.
[8]
Pedersen, T. B., Jensen, C. S., and Dyreseon C. E.. Supporting Imprecision in Multidimensional Data bases Using Granularities. Eleventh International Conference on Scientific and Statistical Database Management, 1999.
[9]
Inmon W., Building the data warehouse, John Wiley &Sons, 1996.
[10]
Kimball R., The Data warehouse Toolkit, John Wiley & Sons, 1996.
[11]
Jarke M., Lenzerini M., Vassiliou Y., Vassiliadis P., Fundamentals of Data Warehouses, Springer-Verlag, 1998.
[12]
Sapir, L., Shmilovici A., and Rokach, L.. A Methodology for the Design of a Fuzzy Data Warehouse. InIntelligent Systems, 2008. IS’08.4th Internationa lIEEE Conference, volume 1, 2008.
[13]
Inmon W., “The operational Data Store”, White Paper, www. billinmon.com/library/whiteprs/earlywp/ttods.pdf,2000.
[14]
Codd E., Codd S., Salley C., Providing OLAP( On-Line Analytical Processing ) to User-Analysts: An IT Mandate, Report, Arbor Soft ware White Paper,1993.
[15]
L. A. Zadeh. The Concept of a Linguistic Variable and its Applicationt of Approximate Reasoning–PartI. Information Science, (8): 199-249, 1975.
[16]
D. Hareland B. Rumpe. Meaningful Modeling: What’s the Semantics of "Semantics"? Computer, 37(10): 64–72, October 2004.
[17]
Ralph Kimball and Joe Caserta. The Data Warehouse ET L Tool kit. Wiley Publishing, Inc., 2004.
[18]
K. V. N. N. Pavan Kumar, P. Radha Krishna, and Supriya Kumar The Fuzzy OLAP Cube for Qualitative Analysis. InIntelligent Sensing and Information Processing, pages 290–295, 2005.
[19]
Heiko Schepperle, Andreas Merkel, and Alexander Haag. Erhaltvon Imperfektion ineinem Data Warehouse. Internationales Symposium: Data-Warehouse-Systeme and Knowledge - Discovery, 2004.
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