You can assign any label in the model to any reference data value. If the same value has different meanings in different rows of reference data, you can assign a different label to each value in each row.

The range of label values can correspond to the range of input ports that the Labeler transformation or the Parser transformation reads during probabilistic analysis. The probabilistic model must contain at least one label value that the transformation can apply to the data values on each input port.

For example, a warehouse might store inventory data in a comma-delimited file that defines eight columns. You design a mapping that parses the inventory data to a database table. You create a probabilistic model with a label value for each data column. When you run the mapping, the Parser transformation writes each value in the input data to the correct column in the target table.

The following table shows the columns of inventory data and the label values that you might create in a probabilistic model: