How Fuzzy Query Works (part 3)

What our school administrator needs is some way to express the idea of "good grades" and "good attendance" on a sliding scale. This would allow the two ideas to be expressed in terms of degree rather than in absolute terms. 

Suppose student A has a 4.0 GPA and student B has a 3.5 GPA. Both have good grades. However, it is plain to see that only student A fits the ideal of having good grades. In fuzzy logic, we would say that student A has complete membership in the set of students with good grades, while student B only has partial membership in the set of students with good grades. 

Under certain circumstances, students with grades as low as 3.0 might be considered to have good grades. We certainly would not want to rule any of them out, particularly if there are only a smattering of students in the data set who have grades above 3.5. This idea becomes especially important in situations where more than one column of data is used to define a semantic notion. In this case, data from the GPA column and the ABSENCES column are somehow combined to form the semantic notion "GOOD STUDENT". 

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