Mining Structures in SQL Server Analysis Services Multidimensional Models

Updated: November 17, 2023 | Article

A mining structure is a core object in SQL Server Analysis Services (SSAS) that defines the data used for data mining. It acts as a container for one or more mining models. You can think of a mining structure as the blueprint for data mining; it specifies the data sources, the columns to be included, and how the data should be processed and partitioned for mining tasks.

When you create a mining structure, you essentially create a data mining schema. This schema is then used by one or more mining models to discover patterns and insights within your data. The mining structure defines the scope and attributes of the data that will be analyzed, ensuring consistency and relevance for your data mining projects.

A mining structure is composed of several key components:

• Data Sources: Specifies the tables or views from which the data will be extracted.
• Columns: Defines the attributes (fields) from the data sources that will be used in the mining structure. These columns can be categorized as predictable (target) or input (feature).
• Partitions: Allow you to divide the mining structure into subsets of data. This is useful for creating training and testing sets, enabling more robust model evaluation and preventing overfitting.
• Content: Refers to the actual data that is loaded into the mining structure for analysis.

You can create mining structures using SQL Server Data Tools (SSDT) for Analysis Services. The process typically involves the following steps:

1. Connect to your Analysis Services instance in SSDT.
2. Create a new Analysis Services project or open an existing one.
3. Right-click on the "Mining Structures" folder and select "New Mining Structure."
4. Choose the data mining technique you want to use (e.g., Clustering, Decision Trees, Linear Regression).
5. Select your data source views and specify the columns to include as input and predictable attributes.
6. Configure data transformations and discretizations if necessary.
7. Define partitions for training and testing data.
8. Deploy the mining structure to your Analysis Services server.

A mining structure can contain one or more mining models. Each mining model is built upon the data defined by the mining structure but uses a specific algorithm to discover patterns. For example, a single mining structure containing customer demographic and purchase data could be used to train:

• A clustering model to group similar customers.
• A decision tree model to predict customer churn.
• A regression model to forecast sales.

The relationship between a mining structure and its models is one-to-many. The mining structure provides the foundation, and the models are specialized analytical engines operating on that foundation.

• Data Quality: Ensure the data used for mining structures is clean, accurate, and relevant.
• Feature Selection: Carefully select input columns to avoid noise and improve model performance.
• Data Transformations: Apply appropriate transformations (e.g., normalization, aggregation) to prepare data for specific algorithms.
• Partitioning: Effective partitioning is crucial for evaluating model accuracy and generalization.
• Algorithm Choice: The choice of algorithm is dependent on the business problem you are trying to solve.

• Data Mining Algorithms in SQL Server Analysis Services
• Creating a Clustering Model
• Using Data Mining Queries
• SQL Server Data Tools for Analysis Services