They are different techniques to compare two values using fuzzy logic. FSQL defines fuzzy comparators like FEQ (fuzzy equal), NFEQ (necessarily fuzzy equal), FGT (fuzzy greater than), NFGT (necessarily fuzzy greater than), and so forth.
Published in Chapter:
How to Achieve Fuzzy Relational Databases Managing Fuzzy Data and Metadata
Mohamed Ali Ben Hassine (Tunis El Manar University, Tunisia), Amel Grissa Touzi (Tunis El Manar University, Tunisia), José Galindo (University of Málaga, Spain), and Habib Ounelli (Tunis El Manar University, Tunisia)
Copyright: © 2008
|Pages: 30
DOI: 10.4018/978-1-59904-853-6.ch014
Abstract
Fuzzy relational databases have been introduced to deal with uncertain or incomplete information demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate flexible querying to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smooth migration towards this technology. This chapter presents a migration approach from relational databases towards fuzzy relational databases. This migration is divided into three strategies. The first one, named “partial migration,” is useful basically to include fuzzy queries in classic databases without changing existing data. It needs some definitions (fuzzy metaknowledge) in order to treat fuzzy queries written in FSQL language (Fuzzy SQL). The second one, named “total migration,” offers in addition to the flexible querying, a real fuzzy database, with the possibility to store imprecise data. This strategy requires a modification of schemas, data, and eventually programs. The third strategy is a mixture of the previous strategies, generally as a temporary step, easier and faster than the total migration.