Abstract
In traditional database management systems, imprecision has not been taken into account so one can say that there is some sort of lack of flexibility. The main cause is that queries retrieve only elements which precisely match to the given Boolean query. Many works were proposed in this context. The majority of these works are based on Fuzzy logic. In this paper, we discuss the flexibility in databases by referring to the Formal Concept Analysis theory. We propose an environment based on this theory which permits the flexible modelling and querying of a database with powerful retrieval capability. The architecture of this environment reuses the existing structure of a traditional database and adds new components (Metaknowledge Base, Context Base, Concept Base, etc.) while guaranteeing interoperability between them.
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References
Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems, 4th edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2003)
Galindo, J., Urrutia, A., Piattini, M.: Fuzzy Databases: Modeling, Design, and Implementation. IGI Publishing, Hershey (2006)
Ganter, B., Stumme, G., Wille, R.: Formal Concept Analysis: Foundations and Applications. Springer, Heidelberg (1999)
Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered sets, pp. 445–470. Reidel, Dordrecht (1982)
Stumme, G., Wille, R., Wille, U.: Conceptual knowledge discovery in databases using formal concept analysis methods. In: Principles of Data Mining and Knowledge Discovery, pp. 450–458 (1998)
Priss, U.: Establishing connections between formal concept analysis and relational databases. In: 13th International Conference on Conceptual Structures, ICCS 2005, Kassel, Germany, pp. 132–145 ( July 2005)
Hereth, J.: Relational scaling and databases. In: Priss, U., Corbett, D., Angelova, G. (eds.) ICCS 2002. LNCS (LNAI), vol. 2393, pp. 62–76. Springer, Heidelberg (2002)
Bosc, P., Pivert, O.: Sqlf: a relational database language for fuzzy querying. IEEE Transactions on Fuzzy Systems 3(1), 1–17 (1995)
Ben Hassine, M.A., Ounelli, H., Touzi, A.G., Galindo, J.: A migration approach from crisp databases to fuzzy databases. In: Proc. IEEE International Fuzzy Systems Conference FUZZ-IEEE 2007, London, July 23-26, 2007, pp. 1872–1879 (2007)
Ben Hassine, M.A., Grissa, A., Galindo, J., Ounelli, H.: How to achieve fuzzy relational databases managing fuzzy data and metadata. In: Galindo, J. (ed.) Handbook on Fuzzy Information Processing in Databases. Information Science Reference, vol. 2, pp. 351–380 (2008)
Koyuncu, M., Yazici, A.: Ifood: an intelligent fuzzy object-oriented database architecture 15(5), 1137–1154 (2003)
Medina, J.M., Pons, O., Vila, M.A.: First: A fuzzy interface for relational systems. In: Proceedings of the Sixth International Fuzzy Systems, Association World Congress, Brazil, vol. 2, pp. 409–412 (1995)
Medina, J.M., Pons, O., Cubero, J.C., Miranda, M.A.V.: Freddi: A fuzzy relational deductive database interface. International Journal of Intelligent Systems 12(8), 597–613 (1997)
Medina, J.M., Pons, O., Vila, M.A., Cubero, J.C.: Client/server architecture for fuzzy relational databases. Mathware & soft computing 3(3), 415–424 (1996)
Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Machine Learning 8(1), 87–102 (1992)
Kotsiantis, S., Kanellopoulos, D.: Discretization techniques: A recent survey. GESTS International Transactions on Computer Science and Engineering 32(1), 47–58 (2006)
Liu, H., Hussain, F., Tan, C.L., Dash, M.: Discretization: An enabling technique. Data Min. Knowl. Discov. 6(4), 393–423 (2002)
Hachani, N., Ounelli, H.: Improving cluster method quality by validity indices. In: Wilson, D., Sutcliffe, G. (eds.) FLAIRS Conference, pp. 479–483. AAAI Press, Menlo Park (2007)
Sassi, M., Touzi, A.G., Ounelli, H.: Using gaussians functions to determine representative clustering prototypes. In: DEXA 2006: Proceedings of the 17th International Conference on Database and Expert Systems Applications, pp. 435–439. IEEE Computer Society, Washington (2006)
Stumme, G.: Local scaling in conceptual data systems. In: ICCS 1996: Proceedings of the 4th International Conference on Conceptual Structures, pp. 308–320. Springer, London (1996)
Godin, R., Missaoui, R., Alaoui, H.: Incremental concept formation algorithms based on galois (concept) lattices. Computational Intelligence 11, 246–267 (1995)
Gammoudi, M.M.: Décomposition conceptuelle des relations binaires et ses applications. Habilitation en Informatique, Faculté des Sciences de Tunis (June 2005)
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Ben Hassine, M.A., Ounelli, H. (2008). IDFQ: An Interface for Database Flexible Querying. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds) Advances in Databases and Information Systems. ADBIS 2008. Lecture Notes in Computer Science, vol 5207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85713-6_9
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DOI: https://doi.org/10.1007/978-3-540-85713-6_9
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