# Attribute Discretization: Using the "Equal Areas" Method - Page 5

May 14, 2009

### 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.

Introduction to MSSQL Server Analysis Services Series