Fuzzy Database Modeling of Imprecise and Uncertain Engineering Information

1. Front Matter

   Pages I-XV

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2. Engineering Information Modeling in Databases

   Pages 1-32

3. Information Imprecision and Uncertainty in Engineering

   Pages 33-45

4. Fuzzy Sets and Possibility Distributions

   Pages 47-58

5. The Fuzzy ER/EER and UML Data Models

   Pages 59-77

6. The Fuzzy IDEF1X Models

   Pages 79-88

7. The Fuzzy EXPRESS Model

   Pages 89-136

8. The Fuzzy Logical Databases

   Pages 137-158

9. Conceptual Designs of the Fuzzy Databases

   Pages 159-166

10. Relational and Nested Relational Database Implementations of the Fuzzy IDEF1X and EXPRESS-G Models

    Pages 167-177

11. Object-Oriented Database Implementation of the Fuzzy EXPRESS Model

    Pages 179-204

12. Back Matter

    Pages 205-210

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Computer-based information technologies have been extensively used to help industries manage their processes and information systems hereby - come their nervous center. More specially, databases are designed to s- port the data storage, processing, and retrieval activities related to data management in information systems. Database management systems p- vide efficient task support and database systems are the key to impleme- ing industrial data management. Industrial data management requires da- base technique support. Industrial applications, however, are typically data and knowledge intensive applications and have some unique character- tics that makes their management difficult. Besides, some new techniques such as Web, artificial intelligence, and etc. have been introduced into - dustrial applications. These unique characteristics and usage of new te- nologies have put many potential requirements on industrial data mana- ment, which challenge today’s database systems and promote their evolvement. Viewed from database technology, information modeling in databases can be identified at two levels: (conceptual) data modeling and (logical) database modeling. This results in conceptual (semantic) data model and logical database model. Generally a conceptual data model is designed and then the designed conceptual data model will be transformed into a chosen logical database schema. Database systems based on logical database model are used to build information systems for data mana- ment. Much attention has been directed at conceptual data modeling of - dustrial information systems. Product data models, for example, can be views as a class of semantic data models (i. e.

• Database Modeling
• Engineering Data Management
• Unified Modeling Language (UML)
• database
• databases
• fuzzy
• fuzzy logic
• fuzzy set
• fuzzy sets
• model
• modeling
• object-oriented database
• relational database
• uncertainty

From the reviews:

"The book offers a survey of fuzzy approaches to databases that serve for engineering applications. … The main value of the book is that it describes fuzzy approaches to databases in two levels: conceptual and logical. This makes the book different from other ones in this area and can be recommended to interested readers." (Jaroslav Pokorný, Zentralblatt MATH, Vol. 1125 (2), 2008)

• College of Information Science & Engineering, Northeastern University, Shenyang, Liaoning, People’s Republic of China

  Zongmin Ma

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