Towards a New Extracting and Querying Approach of Fuzzy Summaries

TL;DR

This paper introduces a novel method for extracting and querying fuzzy summaries in relational databases, enhancing flexibility and handling imprecise data through a new technique called fuzzy SAINTETIQ.

Contribution

It presents a new approach for fuzzy data extraction and flexible querying in fuzzy relational databases based on formal concept analysis.

Findings

Fuzzy SAINTETIQ effectively classifies fuzzy data.

The approach improves query flexibility in fuzzy databases.

It offers solutions for repairing and substituting unanswered queries.

Abstract

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.