Procedure: Employ the Equal Areas
Discretization Method for Attribute Members in Analysis Services
When we browse a cube, we typically dimension the members
of one attribute hierarchy by the members of another attribute hierarchy. For
example, we might group customer sales by product purchased, by customer geography
(state, province or country), or by customer gender. However, with certain
types of attributes, it is useful to have Analysis Services automatically
create groupings of attribute members, particularly when large numbers of contiguous
values are involved, based upon the distribution of the members within an
attribute hierarchy.
As an example, let’s assume that we have been approached
by representatives of our hypothetical client, the Adventure Works
organization. These representatives, members of the Sales department of the business,
tell us that one of the existing attributes
of the Customer dimension, Birth Date, does not serve them well in browses and
reports, as the many different dates are simply listed, the members being
derived from the date values in the BirthDate column of the DimCustomer table
that underlies the Analysis Services layer of the business intelligence
solution. (The developer of the cube, having returned to his home country, is
no longer available to assist the Sales department in modifying the Birth Date
presentation into a more useful format.)
We listen to the description of the problem, and then
suggest grouping the Birth Date information via the pre-defined Equal Areas
discretization method offered within Analysis Services. We suggest this as a
starter approach, so that our client colleagues can see how grouping the values
in this manner might make the data more meaningful in browsing / reporting. We
tell them that, once we do this, information consumers who browse the Birth
Dates attribute hierarchy will see the names / values of the groups instead of
the members themselves – as if the Birth Dates are placed into folders that are
automatically named for the date ranges they contain, as we shall see. This
limits the number of levels that are presented to users, which can be less
confusing, and more useful for analysis. (We inform the client
representatives, too, that other methods of discretization can be leveraged if
the Equal Areas method proves less than ideal – we are simply suggesting the Equal
Areas method as a good starting point to illustrate an approach to meeting the
end objective – an approach that we can easily “tweak” once our colleagues
understand the general concept of discretization, and can then make intelligent
choices with regard to specific nuances that they might deem desirable.
Browse the Existing, Ungrouped Members
within the Attribute Hierarchy
We
will begin our practice with the Equal Areas discretization method within the Birth
Date attribute hierarchy of the Customer dimension.
1.
Within the Solution
Explorer, right-click the Customer dimension (expand the Dimensions folder as
necessary).
2.
Click Open on
the context menu that appears, as shown in Illustration 3.
Illustration 3: Opening the Dimension via the Dimension Designer ...
The
tabs of the Dimension Designer open. Let’s look at the current state of the
attribute under consideration, Birth Date.
3.
Click the Browser
tab.
4.
Select Birth
Date within the dropdown Hierarchy selector atop the Browse tab, as depicted in Illustration 4.
Illustration 4: Select the Birth Date Hierarchy to Browse ...
The attributes belonging to the Birth Date attribute hierarchy appear as partially shown in Illustration
5.
Illustration 5: The Member Attributes, Birth Date Attribute Hierarchy of the Customer Dimension (Partial View)
We
note that many contiguous values appear. It is easy to see how grouping these
values into logical “buckets” might make the data easier for information
consumers to analyze.
Add Equal Areas Discretization within
the Attribute Hierarchy
We
will enact the Equal Areas discretization method from the Dimension Structure tab
of the Customer dimension.
1.
Click the Dimension
Structure tab.
2.
Within the Attributes
pane, select Birth Date (the top attribute hierarchy in the pane).
The Properties window for the attribute appears (by default
in the bottom right corner of the design environment), as depicted in Illustration 6.
Illustration 6: The Properties Window for the Birth Date Attribute
3. In the Properties window, click
the setting box to the right of the DiscretizationMethod property (which
currently contains the word “None”), to enable the selector on the right edge
of the box.
4. Select Equal Areas within the
selector, as shown in Illustration 7.
Illustration 7: Changing the DiscretizationMethod Property Setting to “EqualAreas” ...
The DiscretizationMethod property specifies the method
used to group the members of the owner attribute. Once we tell Analysis
Services the method of discretization, we can tell it how many “buckets” to
create for purposes of grouping the attribute members.
5. In the setting box to the right of
the DiscretizationBucketCount property (which currently contains the “0”),
replace the value for the DiscretizationBucketCount property with the number 12.
The DiscretizationBucketCount property specifies the
number of buckets in which to discretize members of the attribute to which the
property belongs. The default setting is “0”. When we leave the DiscretizationBucketCount
property at default, Analysis Services generates the number of groups independently,
after sampling the underlying data involved.
The affected portion of
the Properties window for the Birth Date attribute hierarchy appears as depicted
(modifications surrounded by the red box) in Illustration 8.
Illustration 8: Properties Window for the Birth Date Attribute Hierarchy, with Modifications
We are now ready to process the Analysis Services
database, and then to examine the results of our handiwork with the browser, as
before.
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