Fuzzy Databases Modeling Design And Implementation 1st Edition by Jose Galindo, Angelica Urrutia, Mario Piattini ISBN 1591403243 9781591403241

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ISBN 10: 1591403243 
ISBN 13: 9781591403241
Author: Jose Galindo, Angelica Urrutia, Mario Piattini

Fuzzy Databases: Modeling, Design and Implementation focuses on some semantic aspects which have not been studied in previous works and extends the EER model with fuzzy capabilities. The exposed model is called FuzzyEER model, and some of the studied extensions are: fuzzy attributes, fuzzy aggregations and different aspects on specializations, such as fuzzy degrees, fuzzy constraints, etc. All these fuzzy extensions offer greater expressiveness in conceptual design. Fuzzy Databases: Modeling, Design and Implementation proposes also a method to translate FuzzyEER model to a classical DBMS, and defines FSQL (Fuzzy SQL), an extension of the SQL language that allows users to write flexible conditions in queries, using all extensions defined by the FuzzyEER model. This book, while providing a global and integrated view of fuzzy database constructions, serves as an introduction to fuzzy logic, fuzzy databases and fuzzy modeling in databases.

Fuzzy Databases Modeling Design And Implementation 1st Table of contents:

Part I: Introduction to Fuzzy Databases

1. Introduction to Fuzzy Logic and Databases

   • Basic principles of fuzzy logic.
   • Overview of database systems and their importance.
   • Motivation for combining fuzzy logic with databases.

2. Fuzzy Sets and Fuzzy Relations

   • Fundamental concepts of fuzzy sets.
   • Fuzzy relations and their properties.
   • Applications of fuzzy sets in database systems.

3. Fuzzy Logic in Database Management Systems

   • Integration of fuzzy logic with traditional database management systems (DBMS).
   • Advantages and challenges of fuzzy databases.
   • Comparison with classical database approaches.

Part II: Fuzzy Data Modeling

4. Fuzzy Data Models

   • Overview of fuzzy data models in relational databases.
   • Representation of uncertainty and imprecision in databases.
   • Examples of fuzzy models: fuzzy relational models, fuzzy entity-relationship models.

5. Fuzzy Entity-Relationship Modeling

   • Designing databases using fuzzy entity-relationship (ER) models.
   • Differences between traditional and fuzzy ER models.
   • Use cases and examples in real-world applications.

6. Fuzzy Functional Dependencies and Integrity Constraints

   • Definition and significance of fuzzy functional dependencies.
   • Enforcing fuzzy integrity constraints in database systems.
   • Examples of applying fuzzy dependencies to improve database quality.

Part III: Fuzzy Database Design

7. Fuzzy Schema Design

   • Design of fuzzy database schemas.
   • Translating fuzzy data models into relational schemas.
   • Handling fuzzy attributes and relationships.

8. Normalization in Fuzzy Databases

   • Fuzzy normalization techniques.
   • How fuzzy normalization differs from classical normalization methods.
   • Benefits and challenges in applying normalization to fuzzy databases.

9. Fuzzy Query Processing and Optimization

   • Designing fuzzy queries for relational databases.
   • Fuzzy query operators and their implementation.
   • Query optimization techniques for fuzzy databases.

Part IV: Fuzzy Database Implementation

10. Fuzzy Database Implementation Techniques

    • Implementing fuzzy databases using DBMS platforms.
    • Fuzzy data representation and storage.
    • Case studies of fuzzy database implementations.

11. Fuzzy Query Languages

    • Development and usage of fuzzy query languages.
    • Extensions to SQL for fuzzy data queries.
    • Examples of querying fuzzy data.

12. Fuzzy Database Systems and Applications

    • Real-world applications of fuzzy databases (e.g., decision support systems, expert systems, and data mining).
    • Use of fuzzy logic in data retrieval, classification, and prediction.
    • Case studies and applications in various fields.

Part V: Advanced Topics and Future Directions

13. Advanced Fuzzy Database Models

    • Overview of more complex fuzzy models, such as fuzzy object-oriented databases or fuzzy spatial databases.
    • Hybrid models combining fuzzy and other data types (e.g., temporal or multimedia data).

14. Challenges and Future Trends in Fuzzy Databases

    • Current challenges in fuzzy database research and implementation.
    • Future trends and potential developments in the field of fuzzy databases.
    • Integration of fuzzy databases with emerging technologies like cloud computing and big data.

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