TABLE OF CONTENTS Conceptual data modeling involves conceptual (semantic) data models. The conceptual data models, as described in Chapter 1, provide the designers with powerful mechanisms in generating the most complete specification from the real world. The conceptual data models, e.g., ER/EER and UML, represent both complex structures of entities and complex relationships among entities as well as their attributes. Hence, the conceptual data models play an important role in conceptual data modeling and database conceptual design.
A major goal for database research has been the incorporation of additional semantics into the data model. Classical data models often suffer from their incapability to represent and manipulate imprecise and uncertain information that may occur in many non-traditional applications in the real world (e.g., decision-making and expert systems). In order to deal with complex objects and imprecise and uncertain information in conceptual data modeling, one needs fuzzy extension to conceptual data models, which allow imprecise and uncertain information to be represented and manipulated at a conceptual level.
Fuzzy databases have been extensively studied in last two decades in the context of the relational database model (see Chapter 7). Current efforts have been concentrated on fuzzy object-oriented databases (see Chapter 9). However, less research has been done in modeling imprecision and uncertainty in conceptual data model. In (Zvieli and Chen, 1986), fuzzy set theory was applied to some of the...
