Towards a New Extracting and Querying Approach of Fuzzy Summaries

Diversification of DB applications highlighted the limitations of relational database management system (RDBMS) particularly on the modeling plan. In fact, in the real world, we are increasingly faced with the situation where applications need to handle imprecise data and to offer a flexible querying to their users. Several theoretical solutions have been proposed. However, the impact of this work in practice remained negligible with the exception of a few research prototypes based on the formal model GEFRED. In this chapter, the authors propose a new approach for exploitation of fuzzy relational databases (FRDB) described by the model GEFRED. This approach consists of 1) a new technique for extracting summary fuzzy data, Fuzzy SAINTETIQ, based on the classification of fuzzy data and formal concepts analysis; 2) an approach of assessing flexible queries in the context of FDB based on the set of fuzzy summaries generated by our fuzzy SAINTETIQ system; 3) an approach of repairing and substituting unanswered query.

A Fuzzy Ontology-Based Platform for Flexible Querying

Flexible queries have recently received increasing attention to better characterize the data retrieval. In this paper, a new flexible querying approach using ontological knowledge is proposed. This approach presents an FCA based methodology for building ontologies from scratch then interrogating them intelligently through the fusion of conceptual clustering, fuzzy logic, and FCA. The main contribution is the definition of the ontology rom classes resulting from a preliminary classification of the data and not the initial data. The data cleansing provides a simple ontology and an optimal research of relevant data taking into account the preferences cited by the user in his initial interrogation. To realize this approach, a new platform called “FO-FQ Tab plug-in” is implemented. This plug-in is integrated within the ontology editor Protégé to allow building fuzzy ontologies from large databases and querying them intelligently