State of the Art in Fuzzy Database Modeling

State of the Art in Fuzzy Database Modeling

Jose Galindo (Universidad de Málaga, Spain), Angelica Urrutia (Universidad Católica del Maule, Chile) and Mario Piattini (Universidad de Castilla-La Mancha, Spain)
Copyright: © 2006 |Pages: 15
DOI: 10.4018/978-1-59140-324-1.ch003
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Abstract

On occasion, the term imprecision embraces several meanings that we should differentiate. For example, as you saw in Chapter II, the information you have may be incomplete or fuzzy (diffuse or vague), you may not know whether it is certain (uncertainty), perhaps you are totally ignorant of the information (unknown), you may know that the information cannot be applied to a specific entity (undefined), or you may not even know whether the data can be applied to the entity in question (total ignorance or a value of null) (Umano & Fukami, 1994). Each of these terms depends on the context in which it is applied. The management of uncertainty in database systems is a very important problem (Motro, 1995), as the information is often vague. Motro states that fuzzy information is content-dependent, and he classifies it as follows: • Uncertainty: It is impossible to determine whether the information is true or false. For example, “John may be 38 years old.” • Imprecision: The information available is not specific enough. For example, “John may be between 37 and 43 years old,” “John is 34 or 43 years old” (disjunction), “John is not 37 years old” (negative), or even a simple unknown. • Vagueness: The model includes elements (predicates or quantifiers) that are inherently vague, for example, “John is in his early years” or “John is at the end of his youth.” However, after these concepts have been defined, this case would match the previous one (imprecision). • Inconsistency: It contains two or more pieces of information that cannot be true at the same time. For example, “John is 37 and 43 years old, or he is 35 years old”; this is a special case of disjunction. • Ambiguity: Some elements of the model lack complete semantics (or a complete meaning). For example, “It is unclear whether the salaries are annual or monthly.”

Complete Chapter List

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Dedication
José Galindo, Angelica Urrutia , Mario Piattini
Table of Contents
Foreword
Juan M. Medina
Preface
Leoncio Jiménez
Acknowledgments
Chapter 1
Jose Galindo, Angelica Urrutia, Mario Piattini
This book mixes concepts of different areas of knowledge or technologies, such as databases, system architecture design, SQL language, programming... Sample PDF
Introduction to Fuzzy Logic
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Chapter 2
Jose Galindo, Angelica Urrutia, Mario Piattini
Both the problem of representation and the treatment of imprecise information have been widely discussed. Many references can be found in the... Sample PDF
Fuzzy Database Approaches
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Chapter 3
Jose Galindo, Angelica Urrutia, Mario Piattini
On occasion, the term imprecision embraces several meanings that we should differentiate. For example, as you saw in Chapter II, the information you... Sample PDF
State of the Art in Fuzzy Database Modeling
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Chapter 4
Jose Galindo, Angelica Urrutia, Mario Piattini
In this chapter we present the FuzzyEER Model, which is an extension of the EER Model with fuzzy semantics and notations. The Entity-Relationship... Sample PDF
FuzzyEER: Main Characteristics of a Fuzzy Conceptual Modeling Tool
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Chapter 5
Jose Galindo, Angelica Urrutia, Mario Piattini
The Relational Model was developed by E.F. Codd of IBM and published in 1970. It is currently the most used and has been a milestone in the history... Sample PDF
Representation of Fuzzy Knowledge in Relational Databases: FIRST-2
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Chapter 6
Jose Galindo, Angelica Urrutia, Mario Piattini
This chapter shows the transformation of the FuzzyEER model to a logical design by using relational databases. The FuzzyEER-to-Relational mapping... Sample PDF
Mapping Fuzzy EER Model Concepts to Relations
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Chapter 7
Jose Galindo, Angelica Urrutia, Mario Piattini
The SQL language was essentially developed by Chamberlin and Boyce (1974) and Chamberlin et al. (1976). In 1986, the American National Standard... Sample PDF
FSQL: A Fuzzy SQL for Fuzzy Databases
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Chapter 8
Jose Galindo, Angelica Urrutia, Mario Piattini
The applications of databases are immense. In almost all of them, the advantages of the fuzzy databases can be applied, exploiting their innovative... Sample PDF
Some Applications of Fuzzy Databases With FSQL
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Chapter 9
Jose Galindo, Angelica Urrutia, Mario Piattini
Fuzzy logic (Chapter I) allows us to bring the operation of information systems closer to the working methods of humans. People frequently deal with... Sample PDF
Brief Summary and Future Trends
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Summary of FuzzyEER Model
FRDB Architecture: The FSQL Server
Acronyms and the Greek Alphabet
About the Authors