Browse the Newly Discretized Attribute
with the Dimension Browser
1.
Click the Browser
tab in the Dimension Designer, once again.
2.
Ensure that Birth
Date has remained selected in the Hierarchy selector atop the Browser tab.
3.
Click the Reconnect
button atop the tab, as shown in Illustration 13.
Illustration 13: Partial Browser View - before Reconnecting
The browser details update, and (assuming the All Customers
level remains expanded in the browser), we see twelve groups appear, as
depicted in Illustration 14.
Illustration 14: The Discretized Attribute Member’s Groups
The groups
(ordered date ranges) appear, as expected. Twelve groups have been created,
based upon the algorithm employed by the Equal Areas discretization method.
Browse the Newly Discretized Attribute
with the Cube Browser
Let’s go
one step further and examine the results of our selection of the Equal Areas method from another
practical perspective, that of the cube browser. This will give us an
appreciation for the improvements seen by the information consumers in querying
/ analyzing from the affected data.
1.
Within the Solution
Explorer, once again, right-click the Basic cube (expand the Cubes folder as
necessary).
2.
Click Open on
the context menu that appears, as shown in Illustration 15.
Illustration 15: Opening the Cube via the Cube Designer ...
The
tabs of the Cube Designer open and we arrive, by default, at the Cube Structure
tab.
3.
Click the Browser
tab.
4.
Click the Reconnect
button atop the tab.
5.
In the Metadata
pane, expand the Customer dimension by clicking the “+” sign to its immediate
left.
6.
Expand the
newly exposed Demographic folder.
7.
Right-click
the Birth Date attribute within the expanded Demographic folder.
8.
Select Add to
Row Area from the context menu that appears, as depicted in Illustration 16.
Illustration 16: Adding the Birth Date Attribute to the Browser Row Area ...
We see
all twelve Birth Date buckets appear in the rows of the browser pane.
9.
Click and drag
the Full Name attribute to the immediate right of the physical column
containing the newly placed Birth Date buckets (a line will form at the drop
point), juxtaposing the Customer Names on rows to the immediate right of the Birth
Dates, as shown in Illustration
17.
Illustration 17: Juxtaposing the (Customer) Full Name alongside the Birth Date Groups in Rows ...
All
twelve ranged Birth Date buckets continue to appear in the rows of the browser
pane – with “+” sign “expand” buttons appearing to the immediate left of the
bucket labels.
10.
Expand the 1960-12-10
00:00:00 – 1963-08-11 00:00:00 Birth Date bucket by clicking the “+” sign to the
immediate left of its label.
The selected Birth Date bucket expands, revealing a list
of the customer members whose Birth Dates place them within the time range
defined for the bucket, as partially depicted in Illustration 18.
Illustration 18: Selected Birth Date Bucket (Partial View), Expanded to Show Membership
In what is but one example of how we can use the Birth
Date buckets, we can see lists of customers grouped by Birth Date ranges for
each of the twelve buckets we have created via Equal Areas discretization. We
tell our client colleagues that they might use this arrangement to do far more
than present lists of the members of the various strata. They might also flesh
out the browser with other dimension members (such as Calendar Years, etc.),
drop in various measures (such as Internet Sales and the like), and perform
analysis (as another simple example) upon customer Birth Date when viewed
within the perspective of the sales attributed to those customers.
Having demonstrated the potential effects that we can
achieve using Equal Areas discretization, we turn the development environment
over to the client representatives with which we have worked. Our colleagues
express satisfaction with our efforts, and state that they grasp the concepts
adequately to apply Equal Areas discretization to other attributes within their
cube.
11.
Experiment
further within the browser, as desired.
12.
Select File -> Exit to leave the design environment, when ready,
and to close the Business
Intelligence Development Studio.
Conclusion
In this article, we continued our exploration of attributes
in Analysis Services, this time with the objective of introducing, and gaining
some hands-on exposure to setting up, one of the multiple pre-defined discretization
methods supported within the Analysis Services UDM. We first discussed the
options that are available, and then chose to work with Equal Areas
discretization in the sample cube, to meet the business requirements of a
hypothetical client. (We noted that, in individual articles designed
specifically for the purpose, we examine the setup of other discretization
options, in a manner similar to the one we took here, gaining hand-on exposure
to the use of those options in individual practice scenarios.)
Our examination included a brief, general review of the
purpose of attribute discretization, potential benefits that accrue from discretization
in our UDMs, and how the process can help us to meet the primary objectives of
business intelligence. We performed an overview of the multiple pre-defined discretization
processes supported within the Analysis Services UDM. We then began our
practice session with an inspection, via the browser in the Dimension Designer,
of the contiguous members of a select attribute hierarchy, noting the absence
of grouping and discussing shortcomings of this default arrangement.
Next, we enabled the Equal Areas discretization method
within the dimension attribute Properties pane. We then reprocessed the sample
cube with which we were working to enact the new Equal Areas discretization of
the select attribute members. Finally, we performed further inspections, via
the Dimension Designer and Cube Designer browsers, of the members of the attribute
hierarchy involved in the request for assistance by our hypothetical client,
noting the new, more intuitive grouping established by the newly enacted Equal
Areas discretization method.
About the MSSQL Server Analysis Services Series
This
article is a member of the series Introduction to MSSQL Server Analysis
Services. The series is designed to provide hands-on application of the
fundamentals of MS SQL Server Analysis Services (“Analysis Services”), with
each installment progressively presenting features and techniques designed to
meet specific real-world needs. For more information on the series, please see
my initial article, Creating Our First Cube. For the software components, samples and tools needed
to complete the hands-on portions of this article, see Usage-Based Optimization in Analysis Services 2005, another article within this
series.
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