Creating and Querying Data Mining Models with DMX: Tutorials (Analysis Services - Data Mining)
Topic Status: Some information in this topic is preview and subject to change in future releases. Preview information describes new features or changes to existing features in Microsoft SQL Server 2016 Community Technology Preview 2 (CTP2).
After you have created a data mining solution by using Microsoft SQL Server Analysis Services, you can create queries against the data mining models to predict trends, retrieve patterns in the data, and measure the accuracy of the mining models.
The step-by-step tutorials in the following list will help you learn how to build and run data mining queries by using Analysis Services so that you can get the most from your data.
This tutorial walks you through the creation of a new mining structure and mining models by using the Data Mining Extensions (DMX) language, and explains how to create DMX prediction queries.
This tutorial uses a typical market basket scenario, where you find associations between the products that customers purchase together. This tutorial also demonstrates how to use nested tables when you create a mining structure. You build and train a model based on this structure, and then create predictions using DMX.
This tutorial creates a forecasting model to illustrate the use of the CREATE MODEL (DMX) statement. You then add related models and customize the behavior of each by changing the parameters of the Microsoft Time Series algorithm. Finally you create predictions and update the predictions with new data. The ability to update a time series while making predictions was added in SQL Server 2008.
This tutorial introduces basic concepts, such as how to create a project and how to build mining structures and mining models.
This tutorial contains a number of independent lessons, each introducing you to a different model type. Each lesson walks you through the process of creating a model, exploring the model, and then customizing the model and creating prediction queries.