A fuzzy classification database schema R(A,C,X,T) is a database schema with a set of attributes A, a set of associated contexts C, a set of linguistic variables X, and a set of corresponding terms T. Each linguistic variable Xi has an associated set of terms T(Xi):={T1,…,Tk}. Note that the number of terms depends on the linguistic variable, that is, all linguistic variables must not have the same number of terms.
Published in Chapter:
Fuzzy Classification on Relational Databases
Andreas Meier (University of Fribourg, Switzerland), Günter Schindler (Galexis AG, Switzerland), and Nicolas Werro (University of Fribourg, Switzerland)
Copyright: © 2008
|Pages: 29
DOI: 10.4018/978-1-59904-853-6.ch023
Abstract
In practice, information systems are based on very large data collections mostly stored in relational databases. As a result of information overload, it has become increasingly difficult to analyze huge amounts of data and to generate appropriate management decisions. Furthermore, data are often imprecise because they do not accurately represent the world or because they are themselves imperfect. For these reasons, a context model with fuzzy classes is proposed to extend relational database systems. More precisely, fuzzy classes and linguistic variables and terms, together with appropriate membership functions, are added to the database schema. The fuzzy classification query language (fCQL) allows the user to formulate unsharp queries that are then transformed into appropriate SQL statements using the fCQL toolkit so that no migration of the raw data is needed. In addition to the context model with fuzzy classes, fCQL and its implementation are presented here, illustrated by concrete examples.