Minyar Sassi-Hidri

Sonia Alouane-Ksouri, Minyar Sassi-Hidri, Kamel Barkaoui

In a context of document co-clustering, we define a new similarity measure which iteratively computes similarity while combining fuzzy sets in a three-partite graph. The fuzzy triadic similarity (FT-Sim) model can deal with uncertainty offers by the fuzzy sets. Moreover, with the development of the Web and the high availability of storage spaces, more and more documents become accessible. Documents can be provided from multiple sites and make similarity computation an expensive processing. This problem motivated us to use parallel computing. In this paper, we introduce parallel architectures which are able to treat large and multi-source data sets by a sequential, a merging or a splitting-based process. Then, we proceed to a local and a central (or global) computing using the basic FT-Sim measure. The idea behind these architectures is to reduce both time and space complexities thanks to parallel computation.

Olfa Arfaoui, Minyar Sassi-Hidri

To date, most of the XML native databases (DB) flexible querying systems are based on exploiting the tree structure of their semi structured data (SSD). However, it becomes important to test the efficiency of Formal Concept Analysis (FCA) formalism for this type of data since it has been proved a great performance in the field of information retrieval (IR). So, the IR in XML databases based on FCA is mainly based on the use of the lattice structure. Each concept of this lattice can be interpreted as a pair (response, query). In this work, we provide a new flexible modeling of XML DB based on fuzzy FCA as a first step towards flexible querying of SSD.

Hanene Rezgui, Minyar Sassi-Hidri

This article addresses the problem of expressing preferences in flexible queries while basing on a combination of the fuzzy logic theory and Conditional Preference Networks or CP-Nets.