fCQL is a data analysis tool that allows users to query a predefined fuzzy classification of relational databases. In contrast to the fuzzy query languages, the user does not need to deal with a fuzzy SQL or with fuzzy predicates, which could lead to varying semantics and different interpretations of the original data collection. From the user’s point of view, fCQL can be seen as a human-oriented query language as it functions at the linguistic level. The language can be applied without numerical values through the use of predefined linguistic variables and their associated verbal terms. In this way, the user can easily formulate classification queries as they are intuitive; that is, the meaning of the queries is linguistically expressed.
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.