Predictor Tables in the Data Warehouse

You build cases from the data stored in the Data Warehouse database. The Data Warehouse stores data from your Web site. Each case used to build analysis models is based on particular information in the Data Warehouse. For example, to predict which products specific users are likely to purchase, the cases used to build the analysis models would be based on data tables or views (virtual tables that represent the data in one or more tables in an alternative way) containing information about your users, such as user profiles, purchase history, and/or click history.

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  • To save processing time, it is recommended to build analysis models in views if possible.

The Data Warehouse database also stores the model configurations provided by Commerce Server. The model configurations are stored in four main tables:

  • PredictorModelCfgs. Lists the existing model configurations and its source Web sites.
  • PredictorDataTables. Lists and describes the tables containing the data to be mapped into cases.
  • PredictorModels. Stores the actual analysis models and their properties.
  • PredictorAttributes <configuration name>. Defines the attributes of the case properties, and its distribution. Indicates which properties are to be used to build the model or which properties you want to be predicted. Entries in this table are optional and can be used to override the automatic assignment of property distributions.

For information about viewing the model configuration tables using Microsoft SQL Server, see Viewing Analysis Model Configuration Tables.

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  • If both one dense and one or more sparse tables are used, only data corresponding to users listed in the dense table will be processed. If you want to use sparse data for users without corresponding information in the dense table, you must create records in the dense table for these users with all unknown values set to NULL. For information about the procedure for setting up your own configuration, see Predictor Schema.
  • If you have a number of sparse tables, but no dense table, then only data corresponding to users from the first sparse table will be processed.

See Also

Dense Table

Sparse Table

Workflow for the Predictor Resource

About the Predictor Resource

Analysis Models

Analyzing Model Effectiveness

Using Dependency Network View

Using Decision Tree View

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