FuzzyEER

Abstract

In this chapter we present the FuzzyEER Model, which is an extension of the EER Model with fuzzy semantics and notations. The Entity-Relationship Model was introduced by Chen (1976). Since then, numerous modifications and extensions of its modeling capabilities have been suggested. We will mainly use the approach by Elmasri and Navathe (2000) because it is very popular, general, and has an international scope. With regard to the fuzzy attributes, the following aspects will be defined in the FuzzyEER Model: imprecise attributes, fuzzy attributes associated to one or more attributes or with an independent meaning, as well as degrees of fuzzy membership to the model itself. Furthermore, the following concepts will also be defined: fuzzy aggregation, fuzzy entity, weak fuzzy entity, fuzzy relationship, and defined specialization with fuzzy degrees. Fuzzy constraints are very important, and we review them in this chapter as well.

A Systematic Overview of Fuzzy Extended Entity Relationship (Fuzzy EER) Model Using Practical Examples

“…Several models used for handling uncertainty are: The Null values by Codd, statistical and probability models, basic fuzzy database models (with only one simple fuzzy degree), as well as the models proposed by Buckles-Petry, Prade-Testemale, Umano-Fukamil and GEFRED (Urrutia et al, 2002). GEFRED (Generalized Model for Fuzzy Relational Databases) is a conceptual framework for fuzzy representation proposed by Medina (Galindo et al, 2006;Urrutia et al, 2002). These models allow the storage and use of imprecise information based on the concept of fuzzy information.…”

“…Several of the most suitable applications that use fuzzy information in entities or relationships are the fuzzy values in the attributes (an attribute assumes a value from a fuzzy set), a degree in each attribute value, a degree in all the instances of the entity, a degree in a set of values of diverse attributes, fuzzy degree with its own meaning and fuzzy degree to the model. For each of these cases mentioned above, (Galindo et al, 2006 andUrrutia et al, 2002) proposed its representation in a fuzzy conceptual EER model.…”

“…The next classification is based on criteria for representing and dealing with vague data. Classification is carried out according to the type of domain that underlies the attributes and whether they allow the representation of vague data or only imprecise processing of precise data (Galindo et al, 2006;Urrutia et al, 2002;Ma et al, 2001 andElmasri &Navathe, 2000). There are four types of attributes, namely:…”

“…1, 2, 3 and 4 (Galindo et al, 2006 andUrrutia et al, 2002) • Example 1. Let the Actor be an entity with the following characteristics or attributes: {DNI, Height, Age, Haircolour, Skin-colour}.…”

“…cannot be treated using the classic or traditional conceptual data models but can be manipulated with the use of "fuzzy" semantics and concepts. The "Fuzzy" concept allows for the efficient management and processing of vague and imprecise data (Galindo et al, 2006;Urrutia et al, 2002;Zadeh, 1965;Chen, 1998 and. The word "imprecision" applies to different information management circumstances.…”