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title Creating a Solution and Data Source (Intermediate Data Mining Tutorial) | Microsoft Docs
ms.custom
ms.date 06/13/2017
ms.prod sql-server-2014
ms.reviewer
ms.technology
analysis-services
ms.topic conceptual
ms.assetid 0488b231-1045-4169-aabb-c1005d86ca30
author minewiskan
ms.author owend
manager craigg

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

To work with data mining, you must first create a project in [!INCLUDEssBIDevStudioFull] using the template, Analysis Services Multidimensional and Data Mining Project. When you open the template, it loads into the designer all the schemas that you might need for data mining: data sources, mining structures and mining models, and even cubes if your mining structure uses multidimensional data.

When you create the project, your solution is stored as a local file until the solution is deployed. When you deploy the solution, [!INCLUDEssASnoversion] looks for the [!INCLUDEssASnoversion] server specified in the project properties, and creates a new [!INCLUDEssASnoversion] database with the same name as the project. By default, [!INCLUDEssASnoversion] uses the localhost instance for new projects. If you are using a named instance, or if you 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.

For more information about [!INCLUDEssASnoversion] projects, see Create an Analysis Services Project (SSDT).

To create a new Analysis Services project for this tutorial

  1. Open [!INCLUDEssBIDevStudioFull].

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

  3. Select Analysis Services Multidimensional and Data Mining Project from the Installed Templates pane.

  4. In the Name box, name the new project DM Intermediate.

  5. [!INCLUDEclickOK]

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

  1. In [!INCLUDEssBIDevStudioFull], 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. If you are using a named instance of [!INCLUDEssASnoversion], type the machine name and then the instance name. [!INCLUDEclickOK]

To change the deployment properties for a project (optional)

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

    -- or --

    In [!INCLUDEssBIDevStudioFull], 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 objects.

Creating a Data Source

In the Basic Data Mining Tutorial, you created a data source that stores connection information for the [!INCLUDEssSampleDBDWobject] database. Follow the same steps to create the [!INCLUDEssSampleDBDWobject] 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 [!INCLUDEmsCoName] [!INCLUDEssNoVersion] [!INCLUDEssASnoversion] database together with the data mining models that you create, as a best practice you should include in each data source view only those tables that are required for each data mining model or group of models.

In the following lessons, you will add data source views to support each of the new scenarios. Only the market basket and sequence clustering lessons use the same data source view; otherwise, each scenario uses a different data source view, so the lessons are independent of each other and can be completed separately.

Scenario Data included in the data source view
Lesson 2: Building a Forecasting Scenario (Intermediate Data Mining Tutorial) Monthly sales reports for bicycle models in different regions, collected as a single view.
Lesson 3: Building a Market Basket Scenario (Intermediate Data Mining Tutorial) A table containing a list of customer orders, and a nested table showing the individual purchases for each customer.
Lesson 4: Building a Sequence Clustering Scenario (Intermediate Data Mining Tutorial) The same data that is used for the market basket analysis, with the addition of an identifier that shows the order in which items were purchased.
Lesson 5: Building Neural Network and Logistic Regression Models (Intermediate Data Mining Tutorial) A single table containing some preliminary performance tracking data from a call center.

Next Lesson

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

See Also

Data Mining Projects
Data Source Views in Multidimensional Models