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Creating a Solution and Data Source (Intermediate Data Mining Tutorial)

Each Microsoft SQL Server Analysis Services project defines the schema for the objects in a single Analysis Services database. An Analysis Services database contains mining structures and mining models, Online Analytical Processing (OLAP) cubes, and supplemental objects such as data sources and data source views.

When you create an Analysis Services project by using Business Intelligence Development Studio, your solution is stored as a local file until the solution is deployed. When you deploy the solution, Analysis Services automatically creates a new database that has the same name as the project. By default, Analysis Services uses the localhost instance for new projects. If you are using a named instance, or specified a different name for the default instance, you must change the deployment database property of the project to the location where you want to create your data mining objects.

After the database has been created, you can browse it, view models, or create predictions by using either SQL Server Management Studio or Business Intelligence Development Studio. You can continue to add new objects to this database either by using Analysis Management Objects (AMO) or by re-opening the project file in Business Intelligence Development Studio. Alternatively, you can create a new project but change the database name. This will enable you to reuse an existing database and add new data mining objects to it, rather than creating a new Analysis Services database. However, if you do so, you should check the project properties to ascertain whether the existing database will be updated, or whether it will be overwritten.

For more information about Analysis Services projects, see Defining an Analysis Services Project.

To create a new Analysis Services project for this tutorial

  1. Open Business Intelligence Development Studio.

  2. On the File menu, point to New, and then click Project.

  3. Verify that Analysis Services Project is selected in the Templates pane.

  4. In the Name box, name the new project DM_Intermediate.

  5. Click OK.

To change the instance where data mining objects are stored (optional)

  1. In Business Intelligence Development Studio, on the Project menu, click Properties.

  2. In the left side of the Property Pages pane, click Deployment.

  3. Verify that the Server name is localhost. If you are using a different instance, type the name of the instance. Click OK.

To change the deployment properties for a project (optional)

  1. In Solution Explorer, right-click the project, and select Properties.

    -- or --

    In Business Intelligence Development Studio, on the Project menu, select Properties.

  2. In the left side of the Property Pages pane, click Deployment.

    In the Options pane, select Deployment Mode, and set the options to Deploy All to overwrite, or to Deploy Changes Only to update objects or add new objects.

In the Basic Data Mining Tutorial, you created a data source that stores connection information for the AdventureWorksDW2008 database. Follow the same steps to create the AdventureWorksDW2008 data source in this solution.

To create a data source

A single data source can support multiple data source views, and each data source view can have multiple tables. However, because the data source and data source view are deployed to your Microsoft SQL Server Analysis Services database together with the data mining models that you create, as a best practice you should include in the data source view only those tables that are needed for each data mining model or group of models.

In the following lessons, you will add new data source views to support the new mining structures and models that you design in each lesson. The market basket and sequence clustering lessons use the same data source, but in all other respects, the lessons are independent of each other and can be completed separately.

Lesson

Data Source view

Lesson 2: Building a Forecasting Scenario (Intermediate Data Mining Tutorial)

Monthly sales reports for bicycle versions in different regions.

Lesson 3: Building a Market Basket Scenario (Intermediate Data Mining Tutorial)

Data from analysis of customer purchases. This data source view includes a nested table.

Lesson 4: Building a Sequence Clustering Scenario (Intermediate Data Mining Tutorial)

Data from analysis of customer purchases, enhanced by addition of a sequence identifier.

Lesson 5: Building Neural Network and Logistic Regression Models (Intermediate Data Mining Tutorial)

Preliminary performance tracking data from a call center.

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