Advanced Modeling - Microsoft SQL Server 2008 Analysis Services - page 93

</Source>
</KeyColumn>
</KeyColumns>
<NameColumn>
<DataType>WChar</DataType>
<Source xsi:type=”ColumnBinding”>
<TableID>dbo_customer</TableID>
<ColumnID>customer_id</ColumnID>
</Source>
</NameColumn>
<DiscretizationMethod>Automatic</DiscretizationMethod>
</Attribute>

For a user-defined method, you define the boundaries for every group that you specify. In
this process, you have to define the binding of the attribute to the data source. (For more
information about attribute bindings, see Chapter 18, “DSVs and Object Bindings.”)

When you create attribute groups, it's helpful to give them names that are intuitive for
users. Analysis Services has templates that can help generate names for the groups.

You can also use attribute discretization for attributes that are already discrete but that
have a large number of values, such as the CustomerID attribute in the Customer dimen-
sion. You can use a discretization method to group these attribute members into groups
that are more easily usable for analysis.

Indirect Dimensions

All the measure group dimensions we've discussed so far have been direct. Not only are
they direct, they are also regular. Their granularity attributes directly define how data is
loaded into the fact. (See the section “Granularity of Fact” in Chapter 7, “Measures and
Multidimensional Analysis.”)

However, Analysis Services supports two types of measure group dimensions:

. Direct , which are directly related to the fact

. Indirect , which are bound to the fact through other dimensions or measure groups

Indirect dimensions do not define how data is loaded into the fact. Whether you include
an indirect dimension in the fact affects only the logical representation of data in the
cube. Adding or removing an indirect dimension doesn't require reloading data in the
fact. Because you don't have to reload the data into the fact, you can change the number
of dimensions you use to analyze the data in the measure group without reloading that
data. We have three types of indirect dimensions:

. Referenced dimensions

. Many-to-many dimensions

. Data-mining dimensions (not discussed in this topic)