Managing Mining Models

This document provides comprehensive guidance on managing mining models within SQL Server Analysis Services (SSAS). Effective management is crucial for the performance, accuracy, and usability of your data mining solutions.

Overview of Mining Model Management

Managing mining models involves several key aspects, including creation, configuration, processing, updating, and deleting models. Understanding these operations ensures that your data mining solutions remain relevant and efficient.

Creating and Configuring Mining Models

When you create a mining model, you associate it with a mining structure. The model uses the columns defined in the structure and applies a specific mining algorithm. Key configuration settings include:

• Algorithm Selection: Choosing the appropriate algorithm (e.g., Linear Regression, Logistic Regression, Decision Trees, Clustering, Neural Networks) based on your business problem.
• Algorithm Parameters: Tuning algorithm-specific parameters to optimize model performance and accuracy.
• Input and Predictable Columns: Defining which columns from the mining structure will be used as input for the model and which columns the model will predict.

Processing Mining Models

After a mining model is created, it needs to be processed. Processing involves training the model using the data from the associated mining structure. The processing step analyzes the data and builds the internal structures of the model.

You can process a mining model using SQL Server Management Studio (SSMS) or programmatically via AMO (Analysis Management Objects) or XMLA (XML for Analysis).

Updating and Retraining Models

Data is dynamic, and business requirements evolve. Therefore, it's often necessary to update or retrain your mining models to maintain their accuracy and relevance. This can involve:

• Incremental Training: Adding new data to an existing model without retraining from scratch. This is efficient for large datasets.
• Full Retraining: Completely rebuilding the model with a new dataset or updated parameters.

The decision to update or retrain depends on the nature of the data changes and the impact on model performance.

Deleting Mining Models

You may need to delete mining models that are no longer in use or are being replaced by newer versions. Deleting a model removes it from the Analysis Services database. Be cautious, as this action is irreversible.

You can delete models through SSMS or programmatically.

Monitoring and Performance Tuning

Regular monitoring of your mining models is essential. Key metrics to track include:

• Accuracy: How well the model predicts outcomes.
• Performance: The speed at which the model can make predictions.
• Data Drift: Changes in the input data distribution over time that might affect model accuracy.

Performance tuning might involve optimizing algorithm parameters, adjusting the mining structure, or ensuring efficient data processing.

Example: Basic SQL Server Management Studio Workflow

1. Connect to your SQL Server Analysis Services instance in SSMS.
2. Navigate to your Analysis Services database.
3. Right-click on the desired Mining Structure and select "New Mining Model...".
4. Choose your mining algorithm and configure its settings.
5. Define input and predictable columns.
6. Complete the wizard to create the model.
7. Right-click on the newly created Mining Model and select "Process" to train it.