Management of Data Mining Solutions and Objects
Applies to: 
SQL Server 2019 and earlier Analysis Services 
Azure Analysis Services
Fabric/Power BI Premium
Important
Data mining was deprecated in SQL Server 2017 Analysis Services and now discontinued in SQL Server 2022 Analysis Services. Documentation is not updated for deprecated and discontinued features. To learn more, see Analysis Services backward compatibility.
SQL Server 2017 provides client tools that you can use to manage existing mining structures and mining models. This section describes the management operations that you can perform using each environment.
In addition to these tools, you can manage data mining objects programmatically, by using AMO, or use other clients that connect to an SQL Server Analysis Services database, such as the Data Mining Add-ins for Microsoft Excel 2007.
In this Section
Processing Requirements and Considerations (Data Mining)
Using SQL Server Profiler to Monitor Data Mining (Analysis Services - Data Mining)
Location of Data Mining Objects
Mining structures and models that have been processed are stored in an instance of SQL Server Analysis Services.
If you create a connection to an SQL Server Analysis Services database in Immediate mode when developing your data mining objects, any objects that you create are immediately added to the server as you work. However, if you design data mining objects in Offline mode, which is the default when you work in SQL Server Data Tools, the mining objects that you create are only metadata containers until you deploy them to an instance of SQL Server Analysis Services. Therefore, any time that you make a change to an object, you must redeploy the object to the SQL Server Analysis Services server. For more information about data mining architecture, see Physical Architecture (Analysis Services - Data Mining).
Note
Some clients, such as the Data Mining Add-ins for Microsoft Excel 2007, also let you create session mining models and mining structures, which use a connection to an instance but store the mining structure and models on the server only for the duration of the session. You can still manage these models through the client, the same as you would structures and models stored in an SQL Server Analysis Services database, but the objects are not persisted after you disconnect from the instance of SQL Server Analysis Services.
Managing Data Mining Objects in SQL Server Data Tools
SQL Server Data Tools offers features that make it easy to create, browse, and edit data mining objects.
The following links provide information on how you can modify data mining objects by using SQL Server Data Tools:
Typically you will use SQL Server Data Tools as a tool for developing new projects and adding to existing projects, and then manage projects and objects that have been deployed by using tools such as SQL Server Management Studio.
However, you can directly modify objects that are already deployed to an instance of ssASnoversion by using the Immediate option and connecting to the server in Online mode. For more information, see Connect in Online Mode to an Analysis Services Database.
Warning
All changes to a mining structure or mining model, including changes to metadata such as a name or description, require that the structure or model be reprocessed.
If you do not have the solution file that was used to create the data mining project or objects, you can import the existing project from the server using the Analysis Services Import wizard, make modifications to the objects, and then redeploy using the Incremental option. For more information, see Import a Data Mining Project using the Analysis Services Import Wizard.
Managing Data Mining Objects in SQL Server Management Studio
In SQL Server Management Studio, you can script, process, or delete mining structures and mining models. You can view only a limited set of properties by using Object Explorer; however, you can view additional metadata about mining models by opening a DMX Query window and selecting a mining structure.
Managing Data Mining Objects Programmatically
You can create, alter, process, and delete data mining objects by using the following programming languages. Each language is designed for different tasks and as a result, there might be restrictions on the type of operations that you can perform. For example, some properties of data mining objects cannot be changed by using Data Mining Extensions (DMX); you must use XMLA or AMO.
Analysis Management Objects (AMO)
Analysis Management Objects (AMO) is an object model built on top of XMLA that gives you full control over data mining objects. By using AMO, you can create, deploy, and monitor mining structures and mining models
Restrictions: None.
Data Mining Extensions (DMX)
Data Mining Extensions (DMX) can be used with other command interfaces such as ADO.NET or ADOMD.Net to create, delete, and query mining structures and mining models.
Restrictions: Some properties cannot be changed by using DMX.
XML for Analysis (XMLA)
XML for Analysis (XMLA) is the data definition language for all of Analysis Services. XMLA gives you control over most data mining objects and server operations. All management operations between the client and the server can be performed by using XMLA. For convenience, you can use the SQL Server Analysis Services Scripting Language (ASSL) to wrap the XML.
Restrictions: SQL Server Data Tools generates some XMLA statements that are supported for internal use only, and cannot be used in XML DDL scripts.