Reviewing and revising the attributes for a new data set

When you create a new data set, you can adjust the attributes that are derived from the file or data source.

Excluding attributes from a new data set
By default, when you create a new data set, it includes all of the available attributes.
Editing the attribute names for a new data set
In the list of attributes for a new data set, the Attribute Name column contains an editable text field with the name of the attribute. When you review the attribute list, you can change the name assigned to each attribute.
Changing an attribute's data type
In the list of attributes for a new data set, the Data Type column lists the data type for the attribute. The data type controls the available formatting and aggregation options for the attribute value.
Handling invalid or empty values
For each attribute in a new data set, you can indicate how to handle invalid or empty values. Invalid values can only occur to attributes with a type other than String, and are values whose format does not match the attribute data type, and cannot be converted to that data type. For example, for a numerical attribute, some records might have "None" or "null" as the value.
Creating multi-value attributes by splitting or merging attribute values
When creating a new data set, you can create multi-value attributes, either by splitting an attribute value based on a delimiter, or by merging values from multiple attributes.
Selecting the refinement behavior for an attribute
For attributes in a new data set, you can select how refinement works. The refinement behavior includes whether users can refine by multiple values, and whether multiple values use AND or OR.
Indicating whether an attribute is searchable
For string attributes in a new data set, you can indicate whether to support text search for attribute values.