Database Reference
In-Depth Information
without urgency. Note that subclasses are not fuzzy, because a customer is or is not owner
and/or claimant.
Example 9.
Figure 11 includes another three examples of fuzzy attribute defi ned
specializations using two fuzzy overlapping specializations and one disjointed specializa-
tion. The fi rst one is a specialization with a total participation constraint (double line) and
it establishes that all employees must belong to one or more categories. Besides, Category
is a fuzzy attribute Type 3.
The second one is a specialization with a fuzzy participation constraint with the fuzzy
quantifi er almost all in the labeled arc: Almost all researches must belong to one or more
research lines. Besides, Research Line is a fuzzy attribute Type 3.
The third one is a disjointed specialization with a total participation constraint and
it establishes that all temporary employees are beginners or seniors, according to the an-
tiquity. Subclasses are not fuzzy because we do not want to store the membership degree.
Besides, a temporary employee cannot belong to both subclasses. The antiquity is a crisp
and known value but we can make fl exible queries using this attribute, i.e., it is a fuzzy
attribute Type 1.
FUZZY CONSTRAINTS IN UNION
TYPES OR CATEGORIES:
PARTICIPATION AND COMPLETENESS
In the EER model we can also fi nd the union types or categories (Elmasri et al., 1985;
2000). It represents the case when some different superclasses may be members of a special
subclass (called category) or not. By defi nition, each member of the subclass or category
must be a member of at least one of the superclasses. Furthermore, in partial categories it
is possible that superclass instances do not belong to the category, because the category is
a subset of the union of all superclasses.
Union types are represented with the union symbol inside a circle. Superclasses are
joined to that circle by a line. Subclass or category is joined to that circle using a single
line with the inclusion symbol. In this type of specialization it is possible to apply fuzzy
constraints in two ways:
1.
Fuzzy participation constraint in one or more superclasses:
This constraint restricts
the number of instances, in the union of any group of superclasses, which belong to the
category. This is represented by an arc labeled with its fuzzy quantifi er, crossing the line
which joins the selected superclass with the circle. Normally, this fuzzy quantifi er will
be relative. For example, with the quantifi er “almost all” on a superclass the constraint
expresses that: “almost all superclass elements belong to the category”. Another option
is to join two or more superclasses with an arc indicating that the union of instances
of those superclasses is constrained in participation. This constraint allows the use of
the (min,max) notation indicating the minimum and maximum number of instances
which belong to the category (using absolute or relative fuzzy quantifi ers).
