Data Mining Tasks in SQL Server Analysis Services

This document outlines the common tasks involved in data mining using SQL Server Analysis Services (SSAS). Data mining allows you to discover patterns, predict future trends, and gain actionable insights from your data.

Understanding Data Mining

Data mining is a process of discovering patterns in large data sets. SQL Server Analysis Services provides a comprehensive set of tools and algorithms to perform various data mining tasks, including:

• Classification: Predicting a categorical outcome.
• Clustering: Grouping similar items together.
• Regression: Predicting a numerical outcome.
• Association Rules: Finding relationships between items.
• Sequence Analysis: Identifying patterns in sequential data.

1. Create a Mining Model

The first step is to define your data mining project and create a mining structure, which serves as a container for your mining models. You'll select the data source, define the cases and predictable attributes, and choose the mining algorithms best suited for your task.

Key Steps:

• Start a new Analysis Services Project in SQL Server Data Tools (SSDT).
• Add a new Mining Structure to the project.
• Select your data source and define the input and predictable columns.
• Choose the appropriate mining algorithm (e.g., Decision Trees, Clustering, Linear Regression).

2. Prepare Data for Mining

The quality of your data significantly impacts the accuracy and effectiveness of your data mining results. Data preparation involves cleaning, transforming, and enriching your data.

Common Data Preparation Tasks:

• Handling Missing Values: Impute or remove records with missing data.
• Outlier Detection: Identify and address extreme values.
• Feature Engineering: Create new attributes from existing ones.
• Data Transformation: Normalize or discretize numerical data.
• Data Aggregation: Summarize data at a higher level.

3. Train a Mining Model

Once the mining structure and data are prepared, you train the mining model. This process involves applying the selected algorithm to your data to discover patterns and build the predictive model.

Process:

1. Right-click the mining structure in SSDT and select "Process".
2. Choose the "Process Full" option to train the model.
3. SSAS will execute the algorithm and store the trained model.

4. Explore and Visualize a Mining Model

After training, you can explore the discovered patterns and relationships using the various viewers available in SSDT.

Available Viewers:

• Decision Tree Viewer: Visualizes decision trees, showing rules and splits.
• Cluster Viewer: Displays clusters, their characteristics, and distributions.
• Linear Regression Viewer: Shows the regression formula and related statistics.
• Association Rules Viewer: Presents common itemsets and their support/confidence.
• Sequence Clustering Viewer: Helps understand sequential patterns.

These viewers allow you to interactively explore the model's findings, identify significant attributes, and understand how the model makes predictions.

5. Predict Data using a Model

You can use the trained mining model to make predictions on new, unseen data. This is typically done using Data Mining Extensions (DMX) queries.

Example DMX Query for Prediction:

SELECT
    [Customer].[LastName],
    [Customer].[FirstName],
    [Sales Predictions].[TotalSales]
FROM
    [Sales Model].Predict ([<Sales Data Source>]) AS [Sales Predictions]
WHERE
    [Sales Predictions].[TotalSales] > 1000;

The Predict function in DMX takes a data source (which can be a table, query, or another mining model) and returns the predicted values based on the trained model.

6. Evaluate Model Performance

It's crucial to assess how well your mining model performs. SSAS provides tools and metrics for evaluating model accuracy and effectiveness.

Common Evaluation Metrics:

• Classification: Accuracy, Precision, Recall, Confusion Matrix.
• Clustering: Silhouette Coefficient, Cluster Separation.
• Regression: Mean Squared Error (MSE), R-squared.
• Association Rules: Support, Confidence, Lift.

The Model Content viewers also offer insights into model accuracy and potential biases.

7. Deploy and Manage Models

Once you are satisfied with a model, you can deploy it to a production Analysis Services instance. This makes the model available for querying and prediction by applications.

Deployment Steps:

1. Deploy the SSAS project to your target server.
2. Ensure the database containing the model is processed.
3. Applications can then connect to the deployed model using DMX queries.

Model Management:

• Regularly re-train models with new data to maintain accuracy.
• Monitor model performance over time.
• Version and archive models as needed.