A fuzzy data warehouse: theoretical foundations and practical aspects of usage

Marek Miłek, Bożena Małysiak-Mrozek, Dariusz Mrozek


Modern analytical tools increasingly make use of new ways of data analysis that base on fuzzy reasoning and fuzzy processing of information. In the paper, we present a Fuzzy Data Warehouse system (FDW), which we have designed and developed. Fuzzy Data Warehouse (FDW) is a data repository, which contains fuzzy data and allows a fuzzy processing of the data. In the paper, we focus on the most important functional features of the FDW system and our newly developed FDW Browser, which is an analytical application adhering to the Fuzzy-OLAP class of data exploration tools.


data warehouse; fuzzy sets; fuzzy logic; decision support systems

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Kimball R., Reeves L., Margy R., Thomthwaite W.: The Data Warehouse Lifecycle Toolkit John Wiley & Sons, 1998.

Ponniah P.: Data Warehousing Fundamentals. A Comprehensive Guide for IT Professionals. John Wiley and Sons, 2001.

Zadeh L.A.: Fuzzy sets. Information and Control. 1965,8 (3), s. 338-353.

Tang X., Chen G.: A complete set of fuzzy relational algebraic operators in fuzzy relational databases. Proceedings of the 2004 IEEE International Conference on Fuzzy Systems, 2004, s. 565-569.

Bose P., Pivert O.: SQLf: A Relational Database Language for Fuzzy Querying. IEEE Transactions on Fuzzy Systems. 1995, Vol. 3, No. 1.

Kacprzyk J., Zadrożny S.: SQLf and FQUERY for Access. IFSA World Congress and 20th NAFIPS International Conference, 2001, s. 2464-2469.

Małysiak B.: Fuzzy Values in SQL Queries Submitted to Databases. Studia Informatica. Vol. 24, No. 2A(53), s. 179-190, Gliwice 2003.

Małysiak B., Mrozek D., Kozielski S.: Processing Fuzzy SQL Queries with Fiat, Context-Dependent and Multidimensional Membership Functions. Proc. of 4th IASTED International Conference on Computational Intelligence (CI 2005), Calgary, Canada. ACTA Press, 2005, s. 36-41.

Chaudhuri S., Ganjam K., Ganti V., Motwani R.: Robust and efficient fuzzy match for online data cleaning. Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data. San Diego, California, 2003, s. 313-324.

Hua-Yang Lin, Ping-Yu Hsu, Gwo-Ji Sheen: A fuzzy-based decision-making procedure for data warehouse system selection. An International Journal of Expert Systems with Applications. 2007, s. 939-953.

Perez D., Somodevilla M.J., Pineda I.H.: Fuzzy Spatial Data Warehouse: A Multidimensional Model. 8th Mexican International Conference on Current Trends in Computer Science, 2007, s. 3-9.

Fasel D., Zumstein D.: A Fuzzy Data Warehouse Approach for Web Analytics. LNCS, Vol. 5736, sp. 276-285. Springer, Heidelberg 2009.

Bouchon-Meunier B., Yager R.R., Zadeh L.A.: Fuzzy logic and soft computing. Advances in Fuzzy Systems, Application and Theory vol. 4, Singapore 1995.

Dubois D., Prade H.: Fundamentals of fuzzy sets. Kluwer Academic Publisher, 2000.

Małysiak-Mrozek B., Mrozek D., Kozielski S.: Data Grouping Process in Extended SQL Language Containing Fuzzy Elements. Advances in Intelligent and Soft Computing Vol. 59, Springer Verlag GmBH, 2009, s. 247-256.

MacQueen J.B.: Some Methods for classification and Analysis of Multivariate Observations. Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967, vol. 1, s. 281-297.

Małysiak-Mrozek B., Mrozek D., Kozielski S.: Processing of Crisp and Fuzzy Measures in the Fuzzy Data Warehouse for Global Natural Resources. LNAI, Springer, Heidelberg 2010, w publikacji.

DOI: http://dx.doi.org/10.21936/si2010_v31.n2A.386