Modelling a Structure of a Fuzzy Data Warehouse

Alain Kuyunsa Mayu; Nathanael Kasoro Mulenda; Rostin Mabela Matendo

doi:doi:10.11648/j.ae.20170102.12

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Modelling a Structure of a Fuzzy Data Warehouse

Alain Kuyunsa Mayu, Nathanael Kasoro Mulenda, Rostin Mabela Matendo

Received: 12 April 2017 Accepted: 22 April 2017 Published: 28 June 2017

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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.

DOI	10.11648/j.ae.20170102.12
Published in	Applied Engineering (Volume 1, Issue 2, December 2017)
Page(s)	48-56
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Target Attribute, Class Membership Attribute, Membership Degree, Membership Degree Attribute,Fuzzy Classification Table, Fuzzy Membership Table

References

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[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’08.4^th 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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Alain Kuyunsa Mayu, Nathanael Kasoro Mulenda, Rostin Mabela Matendo. (2017). Modelling a Structure of a Fuzzy Data Warehouse. Applied Engineering, 1(2), 48-56. https://doi.org/10.11648/j.ae.20170102.12

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ACS Style

Alain Kuyunsa Mayu; Nathanael Kasoro Mulenda; Rostin Mabela Matendo. Modelling a Structure of a Fuzzy Data Warehouse. Appl. Eng. 2017, 1(2), 48-56. doi: 10.11648/j.ae.20170102.12

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AMA Style

Alain Kuyunsa Mayu, Nathanael Kasoro Mulenda, Rostin Mabela Matendo. Modelling a Structure of a Fuzzy Data Warehouse. Appl Eng. 2017;1(2):48-56. doi: 10.11648/j.ae.20170102.12

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@article{10.11648/j.ae.20170102.12,
  author = {Alain Kuyunsa Mayu and Nathanael Kasoro Mulenda and Rostin Mabela Matendo},
  title = {Modelling a Structure of a Fuzzy Data Warehouse},
  journal = {Applied Engineering},
  volume = {1},
  number = {2},
  pages = {48-56},
  doi = {10.11648/j.ae.20170102.12},
  url = {https://doi.org/10.11648/j.ae.20170102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ae.20170102.12},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - Modelling a Structure of a Fuzzy Data Warehouse
AU  - Alain Kuyunsa Mayu
AU  - Nathanael Kasoro Mulenda
AU  - Rostin Mabela Matendo
Y1  - 2017/06/28
PY  - 2017
N1  - https://doi.org/10.11648/j.ae.20170102.12
DO  - 10.11648/j.ae.20170102.12
T2  - Applied Engineering
JF  - Applied Engineering
JO  - Applied Engineering
SP  - 48
EP  - 56
PB  - Science Publishing Group
SN  - 2994-7456
UR  - https://doi.org/10.11648/j.ae.20170102.12
AB  - 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.
VL  - 1
IS  - 2
ER  -

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Author Information

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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APA Style

Alain Kuyunsa Mayu, Nathanael Kasoro Mulenda, Rostin Mabela Matendo. (2017). Modelling a Structure of a Fuzzy Data Warehouse. Applied Engineering, 1(2), 48-56. https://doi.org/10.11648/j.ae.20170102.12

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ACS Style

Alain Kuyunsa Mayu; Nathanael Kasoro Mulenda; Rostin Mabela Matendo. Modelling a Structure of a Fuzzy Data Warehouse. Appl. Eng. 2017, 1(2), 48-56. doi: 10.11648/j.ae.20170102.12

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AMA Style

Alain Kuyunsa Mayu, Nathanael Kasoro Mulenda, Rostin Mabela Matendo. Modelling a Structure of a Fuzzy Data Warehouse. Appl Eng. 2017;1(2):48-56. doi: 10.11648/j.ae.20170102.12

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@article{10.11648/j.ae.20170102.12,
  author = {Alain Kuyunsa Mayu and Nathanael Kasoro Mulenda and Rostin Mabela Matendo},
  title = {Modelling a Structure of a Fuzzy Data Warehouse},
  journal = {Applied Engineering},
  volume = {1},
  number = {2},
  pages = {48-56},
  doi = {10.11648/j.ae.20170102.12},
  url = {https://doi.org/10.11648/j.ae.20170102.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ae.20170102.12},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Modelling a Structure of a Fuzzy Data Warehouse
AU  - Alain Kuyunsa Mayu
AU  - Nathanael Kasoro Mulenda
AU  - Rostin Mabela Matendo
Y1  - 2017/06/28
PY  - 2017
N1  - https://doi.org/10.11648/j.ae.20170102.12
DO  - 10.11648/j.ae.20170102.12
T2  - Applied Engineering
JF  - Applied Engineering
JO  - Applied Engineering
SP  - 48
EP  - 56
PB  - Science Publishing Group
SN  - 2994-7456
UR  - https://doi.org/10.11648/j.ae.20170102.12
AB  - 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.
VL  - 1
IS  - 2
ER  -

Copy | Download