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Posted Jun 23, 2009

Attribute Discretization: Using the "Clusters" Method - Page 5

By William Pearson

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.

Opening the Cube via the Cube Designer ...
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.

Adding the Sick Leave Hours Attribute to the Browser Row Area ...
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.

Juxtaposing the Employee Name alongside the Sick Leave Hours in Rows
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.

Select Sick Leave Hours Buckets, Expanded to Show Membership
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.


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.

» See All Articles by Columnist William E. Pearson, III

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