Towards a New Extracting and Querying Approach of Fuzzy Summaries

Towards a New Extracting and Querying Approach of Fuzzy Summaries

Ines Benali-Sougui (University of Tunis El Manar, Tunisia), Minyar Sassi Hidri (Imam Abdulrahman Bin Faisal University, Saudi Arabia) and Amel Grissa-Touzi (University of Tunis El Manar, Tunisia)
Copyright: © 2018 |Pages: 23
DOI: 10.4018/978-1-5225-5951-1.ch015


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.
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In this section, we present the basic concepts of FDB, Fuzzy attributes in GEFRED model and the theoretical foundations of fuzzy FCA.

Fuzzy Databases (FDB)

In this section, we present the basic concepts of FDB. A FDB is an extension of the relational database. This extension introduces fuzzy predicates under shapes of linguistic expressions that, at the time of a flexible querying, permits to have a range of answers (each one with a membership degree) in order to offer to the user all intermediate variations between the completely satisfactory answers and those completely dissatisfactory (Bosc, P., O., & Litard. L., 1998). The FRDB models are considered in a very simple shape and consist in adding a degree, usually in the interval [0,1], to every tuple. It allows maintaining the homogeneity of the data in DB. The main models are those of PradeTestemale (Prade, H., & C. T., 1987), Umano-Fukami (Umano, M., M. M. K. T., & Fukami, S., 1980), Buckles-Petry (Buckles, B. P. & F. E. P., 1982), ZemankovaKaendel (Zemankova-Leech, 1985) and GEFRED of Medina et al. (1994). This last model constitutes an eclectic synthesis of the various models published so far with the aim of dealing with the problem of representation and treatment of fuzzy information by using relational DB.

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