Logical Data Warehouse Design - Data Warehouse Systems: Design and Implementation - page 117

Chapter 5
Logical Data Warehouse Design
Conceptual models are useful to design database applications since they
favor the communication between the stakeholders in a project. However,
conceptual models must be translated into logical ones for their implemen-
tation on a database management system. In this chapter, we study how
the conceptual multidimensional model studied in the previous chapter can
be represented in the relational model. We start in Sect. 5.1 by describing
the three logical models for data warehouses, namely, relational OLAP
(ROLAP), multidimensional OLAP (MOLAP), and hybrid OLAP (HOLAP).
In Sect. 5.2 , we focus on the relational representation of data warehouses
and study four typical implementations: the star, snowflake, starflake, and
constellation schemas. In Sect. 5.3 , we present the rules for mapping a
conceptual multidimensional model (in our case, the MultiDim model) to the
relational model. Section 5.4 discusses how to represent the time dimension.
Sections 5.5 and 5.6 study how hierarchies, facts with multiple granularities,
and many-to-many dimensions can be implemented in the relational model.
Section 5.7 is devoted to the study of slowly changing dimensions, which
arise when dimensions in a data warehouse are updated. In Sect. 5.8 ,we
study how a data cube can be represented in the relational model and how it
can be queried in SQL using the SQL/OLAP extension. Finally, to show how
these concepts are applied in practice, in Sects. 5.9 and 5.10 ,weshowhow
the Northwind cube can be implemented, respectively, in Microsoft Analysis
Services and in Mondrian.
5.1 Logical Modeling of Data Warehouses
There are several approaches for implementing a multidimensional model,
depending on how the data cube is stored. These approaches are: