SQL Server 2005 : Extending Your Database System with Data Mining - Data Mining Applied (part 2)

2/13/2012 5:21:45 PM

XMLA and Data Mining

To round off our coverage of data mining development and how it compares with OLAP development, it is important to mention that XMLA can be used for all data mining tasks, including execution of a DMX query (of any type) or the scripted creation of objects. As with OLAP objects, you can right-click a mining structure or model in Management Studio’s Object Explorer window and choose Script Mining Structure As (or Script Mining Model As) to generate an XMLA script for its creation, deletion, or alteration. Similarly, you can use the View Code option from the Solution Explorer window in Visual Studio on a mining structure in an SSAS project to reveal the XMLA script that will be executed to deploy that mining structure to the server.

As a quick example, the following query (contained in LeastLikelyCar.xmla in the Management Studio sample solution) executes the same DMX query contained in LeastLikelyCar.dmx and retrieves the result as XML:

<Execute xmlns="urn:schemas-microsoft-com:xml-analysis">
SELECT TOP 250 t.FirstName, t.LastName, t.Phone,
PredictProbability([Is Car Owner],'N') AS ProbNotCarOwner
FROM CarOwner_DT
OPENQUERY([Adventure Works DW],
'SELECT c.FirstName,c.LastName,tc.*
FROM vCustomerProfitability tc
INNER JOIN dimCustomer c ON tc.CustomerKey = c.CustomerKey')
AS t
ON t.Gender = CarOwner_DT.Gender AND
t.IncomeGroup = CarOwner_dt.[Income Group] AND
t.MaritalStatus = CarOwner_dt.[Marital Status] AND
t.Region = CarOwner_dt.Region
ORDER BY PredictProbability([Is Car Owner],'N') DESC

Notice that the structure of the query is virtually identical to the XMLA script QueryCubeChapter18.xmla . The only differences are the change in database name within the <Catalog> tags and the replacement of an MDX query with a DMX query.

Unlike QueryCubeChapter18.xmla, however, this query returns its XML data using a very straightforward schema. Here’s an excerpt from the response stream:

<return xmlns="urn:schemas-microsoft-com:xml-analysis">
<root xmlns="urn:schemas-microsoft-com:xml-analysis:rowset" xmlns:xsi="http://" xmlns:xsd="">
<Phone>1 (11) 500 555-0189</Phone>
<Phone>1 (11) 500 555-0117</Phone>
<Phone>1 (11) 500 555-0173</Phone>

Data Mining and Reporting Services

Once you have designed and trained your mining models, it’s easy to display the results in a Reporting Services report. DMX, although powerful, does lack some basic query functionality that might be desirable for showing data mining results. The ability to write DMX code to aggregate or group results is notably lacking. Because you can specify an SSAS data source in Reporting Services, you can leverage the reporting functionality, including subtotaling and grouping, built into Reporting Services for displaying the results of your mining models.

Return to Visual Studio and create a new Reporting Services project by choosing File, New, Project or File, Add, New Project.

In the New Project or Add New Project dialog box that appears, select Business Intelligence Projects from the Project Types tree view on the left, and select Report Server Project from the Visual Studio Installed Templates list on the right. Name the project Chapter20Reports and then click OK.

You should see a node labeled Chapter20Reports in Solution Explorer. Right-click the Reports node below the Chapter20Reports node, and choose Add, New Item. (Alternatively, you can choose Project, Add New Item or press CTRL+SHIFT+A.)

In the Add New Item dialog box, select Report from the set of Visual Studio Installed Templates. Change the name of the report to Classification Matrix and click Add. Your Visual Studio window should appear similar to Figure 1.

Figure 1. The Report Designer for a new report

On the Data tab, select <New Dataset...> from the Dataset drop-down list.

In the Data Source dialog box, select Microsoft SQL Server Analysis Services from the Type drop-down list and then click the Edit button.

In the Connection Properties dialog box, set the Server Name to the name of your SSAS server, and then select the Chapter20 database from the Select or Enter a Database Name drop-down list. Click OK.

Back in the Select the Data Source screen, change the Name to CustomerProfitMining; the settings should now appear similar to those in Figure 2.

Figure 2. The Data Source dialog box with an SSAS database specified as the source

Click OK to create the data source. You will see the DMX Prediction Query designer render on the Data tab. When you specify an SSAS database as the source for your report, Reporting Services provides a special Analysis Services query designer, which can help you design MDX (OLAP) or DMX (data mining) queries. Because our database has a mining structure and no cubes, the designer defaults to its DMX mode. (Clicking the Command Type MDX toolbar button—the fifth from the left—would allow us to switch to the MDX designer instead.)

On the left of the Data tab is a mini-window labeled Mining Model; click its Select Model button to display the Select Mining Model dialog box. This dialog box displays a tree view of mining structures in the database and the mining models within each.

Expand the CustomerProfitCategory mining structure’s node, select the CustomerProfitCategory_DT model’s node, and click OK.

Back on the Data tab, click the Select Case Table button in the Select Input Table mini-window to bring up the Select Table dialog box. Select the vCustomerProfitability view and then click OK.

Back in the Select Input Table mini-window, click the Select Nested Table button, select the vCustomerPurchases view in the Select Table dialog box, and then click OK once more.

Right-click the Data tab’s designer surface and then choose Modify Connections. In the resulting Modify Mappings dialog box, verify that the mappings between the mining model and the case and nested tables are correct and then click OK. (The Customer Key mining model column will be unmapped and should be left as such.)

We are going to create a report that shows a classification matrix for the CustomerProfitCategory_DT model. The classification matrix shows the actual profit category of customers and the profit category predicted by the Decision Trees model, along with a count of the customers. By looking at the count of cases where the actual category differs from the predicted category, you can get a sense of how well the model predicts profit. In practice, we would create the classification matrix using a test set, such as the random sample of customers in the TestCustomers table. As stated previously, however, we will use vCustomerProfitability, which has the full set of individual customers in AdventureWorksDW, so that the results are reproducible.

In the grid in the bottom half of the Design tab, select vCustomerProfitability from the drop-down list in the first row under the Source column. In the Field column, select the CustomerKey column. (If it is selected by default, do nothing.) In the second row, select vCustomerProfitability again, but this time select ProfitCategory as the field. The predicted profit category will be determined from the CustomerProfitCategory_DT mining model; therefore, in the third grid row, select the CustomerProfitCategory_DT mining model as the source, set Profit Category as the field, and then set its alias to Predicted Profit Category. The Data tab should appear as shown in Figure 3.

Figure 3. The DMX query designer with the Report Designer’s Data tab

To see the actual DMX query generated by the designer, you can deselect the Design Mode toolbar button (first from the right) or right-click anywhere on the Data tab and choose Query. The DMX query generated by the designer will appear at the bottom of the Data tab. You should recognize it as a DMX prediction query—not too dissimilar from ones we wrote in the previous section. In fact, you could paste this query into an empty DMX Query window in SQL Server Management Studio and execute it there. You could also do the reverse—create a report based on the results of one of the prediction queries from the Management Studio Chapter20DMX project by copying the DMX statement directly into the Data tab.

We are now ready to design the layout of the report.

Click the Layout tab. Drag a table item from the Toolbox onto the Layout pane. Right-click the leftmost cell in the Detail row, and choose Properties. Change the Name of the text box to CustomerKey, and select =Fields!CustomerKey.Value from the Value drop-down list. Click OK.

Follow the same steps to add the ProfitCategory field to the middle column of the detail row and the Predicted_Profit_Category field to the right column of the detail row, naming the text boxes ProfitCategory and PredictedProfitCategory, respectively. Figure 4 shows the General tab of the Textbox Properties dialog box for the ProfitCategory field.

Figure 4. The Textbox Properties dialog box

In the Header row, type Customer Key, Profit Category, and Predicted Profit Category above each of the corresponding cells of the Detail row. Format the background color of the Header row and then left-align all cells in the table. When you are finished, the Layout tab should appear similar to Figure 5.

Figure 5. The Layout tab of the Report Designer with a Table item

Click the Preview tab to preview the report. The report lists each customer in vCustomerProfitability along with the customer key, the actual profit category of the customer, and the predicted profit category of the customer.

In its current form, the report is long and unwieldy. To change this, we must group and summarize the records in the table.

Go back to the Layout tab. Right-click the gray row header to the left of the CustomerKey field on the Detail row and then choose Insert Group.

On the General tab of the Grouping And Sorting Properties dialog box, select =Fields!ProfitCategory.Value in the first row and =Fields!Predicted_Profit_Category.Value in the second row of the Expression grid in the Group On section. Click OK.

Back on the Layout tab, you will see two rows added to the table: one above the Detail row and one below the Detail row. Delete the row that was added below the Detail row. (Select its row header and press the DELETE key, or right-click the row header and choose Delete Rows.) Next, right-click the middle cell below the Profit Category heading and then choose Properties. Change the name of the text box to ProfitCategoryGroup and then select =Fields!ProfitCategory.Value from the Value drop-down list. Click OK.

Repeat the previous step for the cell below the Predicted Profit Category heading, naming the text box PredictedProfitCategoryGroup and selecting =Fields!Customer_Profit_Category.Value from the Value drop-down list.

Finally, we want to add a summary measure of the records to the table.

Right-click the gray column header of the Predicted Profit Category column and then choose Insert Column To The Right. Give the new column a header of Count and then type =CountRows() in the cell under the header.

Change the background color of the group header row so that it can be easily identified in the report. Add a Textbox item (from the Toolbox) above the Table object in the report, and enter the text Classification Matrix. Stretch the text box to the full width of the report, center-align it, and format its font and color settings so that the report appears similar to Figure 6.

Figure 6. A table with a group on the Layout tab of the Report Designer

Click the Preview tab. Page through the report. The report is still unwieldy and shows all the data rows. Inserting the group simply reordered the detail rows in the table and added a row before each possible actual and predicted profit category value pair. To fix the report so that it displays a simple classification matrix without the customer details, we must hide the detail rows.

Go back to the Layout tab, right-click the detail row header, and then choose Properties. Expand the Visibility property in the Properties window by clicking its plus sign (+) icon. Set the Hidden property to True and the ToggleItem property to ProfitCategoryGroup. The properties should be set as shown in Figure 7.

Figure 7. Setting the Visibility properties of a table row

Click the Preview tab. You now have your simple classification matrix, as shown in Figure 8.

Figure 8. A classification matrix created using Reporting Services

The classification matrix shows that your Decision Tree model of customer profitability performs pretty well. The first two rows of the classification matrix, where the predicted profit category is equal to the actual profit category, represent the cases in which the Decision Tree model got it right. Out of 18,484 customers (the sum of the Count column), the model correctly predicted the profit category for 17,344 customers (4,346 plus 12,998). Click any profit category’s plus sign to toggle between showing and hiding the detail rows.

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