Abstract This research reports on the synthesis of flexible database querying approach based on ordered lattice theory extension to deal with imprecise and structured data. This approach allows us to construct a multi-attributes type abstraction hierarchy structure for the case of decomposition according several attributes. This structure is defined from an ordered lattice theory extension. Our approach consists of two steps: the first step consists in data organization and the second at seeking, to interrogate them, relevant data sources for a given query. The contributions of this approach are a) the interdependence of the query research criteria, b) the research of the relevant data sources for a given query, and d) the scheduling of the results.

References

[1] Bosc P. and Pivert O., Some Approches for Relational Databases Flexible Querying, International Journal of Intelligent Information Systems , vol.1, no.3-4, pp. 323-354, 1992.

[2] Zadeh L., Fuzzy Sets,". Information and Control 8 , vol. 8, no. 3, pp. 338-353, 1965.

[3] Priss U., Formal Concept Analysis in Information Science, Annual Review of Information Science and Technology (ARIST) , vol. 40, 2006.

[4] Uri K. and Jianjun Z., Fuzzy Clustering Principles, Methods and Examples, IKS , December 1998.

[5] Bosc P., Galibourg M., and Hamon G., Fuzzy Quering with SQL: Extensions and Implementation Aspects, Fuzzy Sets and Systems , vol. 28, pp. 333-349, 1988.

[6] Habib O. and Ramzi B., Interrogation Flexible et Coop rative D'une BD par Abstraction Conceptuelle Hi rarchique, INFORSID , pp. 41-56, 2004 .

[7] Chu W. , Yang H., Chiang K., Minock M., Chow G., and Larson C., CoBase: A Scalable and Extensible Cooperative Information System, Journal of Intelligence Information Systems , Kluwer Academic Publishers, Boston, Mass, vol. 6, no.2-3, pp. 233-259, 1996.

[8] Laszlo S. and Amedeo N., Les Treillis de Galois Pour L organisation et la Gestion des Connaissances, in Proceedings of 11`emes Rencontres de la Soci t Francophone de Classification (SFC 04) , Bordeaux, France, pp. 298-301, 2004.

[9] Halkidi M. and Vazirgiannis M., Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set, in Proceedings of the IEEE International Conference on Data Mining (ICDM.01) , San Jose, California, USA, 2001.

[10] Bezdek J., Pattern Recognition with Fuzzy Objective Function Algorithms , Kluwer Academic Publishers, Nowell, MA, 1981.

[11] Sassi M., Touzi A., and Ounelli H., Using Gaussians Functions to Determine Representative Clustering Prototypes, in Proceedings of the 17 th IEEE International Conference on Database and Expert Systems Applications , Poland, pp. 435-439, 2006.

[12] Ganter B. and Wille R., Formal Concept Analysis: Mathematical Foundations , Springer, Berlin, Heidelberg, 1999.

[13] Wille R., Lattices in Data Analysis: How to Draw them with a Computer, in Rival I. (eds.) , Algorithms and Order, Kluwer, Dordrecht- Boston, pp. 33-58, 1989.

[14] Wolff K., Information Channels and Conceptual Scaling, in Proceedings of the International Conference on Conceptual Structures (ICCS'2000) , Darmstadt, 2000.

[15] Chu W., Chiang K., Hsu C., and Yau H., An Error-Based Conceptual Clustering Method for Providing Approximate Query Answers, Communications of the ACM , vol. 39, no. 12, 1996.

[16] Huynh V. and Nakamori Y., Fuzzy Concept Formation Based on Context Model, in Baba N., et al.(eds) editors, Knowledge-Based Intelligent Information Engineering Systems and Allied Technologie, IOS Press , Amsterdam, pp. 687-691, 2001.

[17] Godin R., Mineau G., and Missaoui R., M thodes de Classification Conceptuelle Bas e sur les Treillis de Galois et Application, Revue D Intelligence Artificielle , vol. 9 no. 2, pp. 105-137, 1995.

[18] Capi pineto C. and Romano G., Order- Theoretical Ranking, Journal of the American Society for Information Science , vol. 51, no. 7, pp. 587-6001, 2000.

[19] Godin R., Missaoui R., and Alaoui H., Incremental Concept Formation Algorithms Based on Galois (Concept) Lattices, Computational Intelligence , vol. 11, no. 2, pp. 246-267, 1995. Minyar Sassi received the diploma of engineering in computer science from National School of Engineering of Tunis, in 2003, and the Master degree in automatic and signal processing from the National School of Engineering of Tunis in 2004. Currently, she is a PhD candidate. Her researches concern query optimization, clustering and flexible querying. Amel Touzi received the diploma of engineering in computer science and PhD in computer science from the Faculty of Sciences of Tunis , in 1989 and 1994, respectively. She is an assistant professor at the Department of Technologies of Information and Communications in the National School of Engineering of Tunis. She is also a member of the Systems and Signal Processing Laboratory 179 The International Arab Journal of Information Technology, Vol. 5, No. 4, October 2008 (LSTS). Her researches interest includes many aspects of logic programming, deductive databases, fuzzy relational databases, object-relational databases flexible querying and artificial intelligence. Habib Oune lli received a PhD in computer science in 1987 from the University of Paris-Sud Orsay, Paris, France. Since 1988, he is a full professor at the Computer Sciences Department of Faculty of Sciences of Tunis. His researches concern fuzzy databases, flexible querying, and deductive databases.