Browse the Newly Discretized Attribute with the Cube Browser
Let’s go
one step further and examine the results of our selection of the Clusters discretization 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 Employee dimension by clicking the “+” sign to its immediate
left.
6.
Expand the
newly exposed Organization folder.
7.
Right-click
the Sick Leave Hours attribute within the expanded Organization folder.
8.
Select Add to
Row Area from the context menu that appears, as depicted in Illustration 16.
Illustration 16: Adding the Sick Leave Hours Attribute to the Browser Row Area ...
We see
all ten Sick Leave Hours buckets appear in the rows of the browser pane.
9.
Click and drag
the Employee Name attribute to the immediate right of the physical column
containing the newly placed Sick Leave Hours buckets (a line will form at the
drop point), juxtaposing the Employee Names on rows to the immediate right of
the Sick Leave Hours, as shown in Illustration 17.
Illustration 16: Juxtaposing the Employee Name alongside the Sick Leave Hours in Rows
All ten Sick
Leave Hours 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 68-80
Sick Leave Hours buckets by clicking the “+” sign to its immediate left of each
label.
The Sick Leave Hours bucket expands, revealing lists of
the employee members whose total Sick Leave Hours place them within the
respective buckets in which they appear, as partially depicted in Illustration 18.
Illustration 18: Select Sick Leave Hours Buckets, Expanded to Show Membership
In what is but one example of how we can use the Sick
Leave Hours buckets, we can see lists of employees that have total Sick Leave
Hours on the books corresponding to each of the ten buckets we have created via
Clusters 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 Reseller
Sales and the like), and perform analysis (as another simple example) upon
employee balances of “unused sick leave” when viewed within the perspective of
the sales figures attributed to those employees, and so forth.
Having demonstrated the potential effects that we can
achieve using Clusters 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 Clusters discretization to other contiguous attributes
within their cubes.
11.
Experiment
further within the browser, as desired.
12.
Select File -à 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 Clusters 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 will examine the setup of other discretization options, in a manner
similar to the one we took here, gaining hands-on exposure to the use of those
options in individual practice scenarios.)
Our examination included a brief, general review of attribute
discretization in Analysis Services, 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 Clusters discretization method within
the dimension attribute Properties pane. We then reprocessed the sample cube
with which we were working to enact the new Clusters 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 Clusters 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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