Authors: Amel Grissa Touzi; Habib Ounalli
Addresses: Department of Technologies of Information and Communications, National School of Engineering of Tunis, University of Tunis El Manar, Tunis, Tunisia. ' Department of Computer Sciences, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis, Tunisia
Abstract: In this paper, we propose our contribution to support flexible query in large database. Our approach proposes to use the generated knowledge result of an algorithm for knowledge discovery in database (KDD). Unfortunately, these algorithms generate big number of rules that are not easily assimilated by the human brain and do not help the user to give semantics of data and to optimise the information research. In this paper, we discuss these problems and we propose a pragmatic solution: 1) by proposing a new approach for KDD through the fusion of conceptual clustering, fuzzy logic and formal concept analysis; 2) by defining a new method to support database flexible querying using the generated knowledge in the first step. This approach cannot be required to modify the SQL language. Also, we prove that this approach is optimum sight that the evaluation of the query is not done on the set of starting data which are enormous but rather by using the set of knowledge on these data.
Keywords: flexible querying; knowledge discovery; databases; formal concept analysis; FCA; fuzzy logic; clustering; flexible queries; database search.
International Journal of Computational Systems Engineering, 2012 Vol.1 No.1, pp.58 - 69
Published online: 23 Aug 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article