Abstract
Currently, educational organizations have a large amount of local information from their academic processes, curricular management processes, diagnostic processes of university admission, among others. Also, educational organizations have access to large external databases with standardized test information that students have taken. In this sense, the data warehouse (DW) is presented as a technology that makes it possible to integrate educational information because it provides for the storage of large amounts of information that come from different sources, in a multidimensionally structured format for historical analysis. In this chapter, the authors present one example of DW applied to the field of education using fuzzy logic. A fuzzy DW will be defined as “a DW that allows storing and operating fuzzy measures, fuzzy relationships between levels and fuzzy levels.” This chapter ends with a critical discussion of the advances and possibilities of technologies such as the DW applied to the field of education.
TopBackground
In this section of the chapter the elements of the FDW are described: Fuzzy Level, Fuzzy Measures, Fuzzy Relationship and Trapezoidal Membership Functions. Also, literature is reviewed.
Key Terms in this Chapter
CWM OLAP Meta Model: CWM OLAP meta model is a standard for representing a multidimensional database.
Fuzzy Attributes TYPE 1: Are classic attributes that support imprecise handling, where the defined linguistic labels will only be used in the fuzzy conditions of the queries.
MDA: Model-driven architecture.
Fuzzy Attributes TYPE 2: Are attributes that support both classical (crisp) and fuzzy (imprecise) data, in the form of possibility distributions over an ordered underlying domain.
DBMS: Data base management systems.
Stereotype: A stereotype allows adding a new semantic meaning to the model element.
Fuzzy Attributes TYPE 3: Are attributes on discrete domain data on underlying unordered domain with analogy.