Chapter 3. Creating and editing DMN models in Business Central

Procedure

1. In Business Central, go to Menu → Design → Projects, click the project name, and select the DMN file you want to modify.

2. In the DMN designer canvas, select a decision node or business knowledge model that you want to define and click the Edit icon to open the DMN boxed expression designer:

   Figure 3.7. Opening a new decision node boxed expression

   

   Figure 3.8. Opening a new business knowledge model boxed expression

   

   By default, all business knowledge models are defined as boxed function expressions containing a literal FEEL expression, a nested context expression of an external JAVA or PMML function, or a nested boxed expression of any type.

   For decision nodes, you double-click the undefined table to select the type of boxed expression you want to use, such as a boxed literal expression, boxed context expression, decision table, or other DMN boxed expression.

   

   For business knowledge models, you right-click the value cell and select the type of expression you want to use, or click Clear and double-click the cleared cell to select a boxed expression of another type.

   

3. For this example, use a decision node and select Decision table as the boxed expression type.

   A decision table in DMN is a visual representation of one or more rules in a tabular format. Each rule consists of a single row in the table, and includes columns that define the conditions (input) and outcome (output) for that particular row.

4. Double-click the input column header to define the name and data type for the input condition. For example, name the input column Credit Score.FICO with a number data type. This column specifies numeric credit score values or ranges of loan applicants.

5. Double-click the output column header to define the name and data type for the output values. For example, name the output column Credit Score Rating and next to the Data Type option, click Manage to create a custom data type with score ratings as constraints.

   

6. In the Manage Custom Data Types window, click Add and create a Credit_Score_Rating data type as a string with the following constraints: "Excellent","Good","Fair","Poor","Bad".

   
7. Click Save to save the data type, and in the decision table, double-click the Credit Score Rating column header and set the data type to this new custom data type.

8. Use the Credit Score.FICO input column to define credit score values or ranges of values, and use the Credit Score Rating column to specify one of the corresponding ratings you defined in the Credit_Score_Rating data type.

   Right-click any value cell to insert or delete rows (rules) or columns (clauses).

   Figure 3.9. Decision node decision table for credit score rating

   

9. After you define all rules, click the top-left corner of the decision table to define the rule Hit Policy and Builtin Aggregator (for COLLECT hit policy only).

   The hit policy determines how to reach an outcome when multiple rules in a decision table match the provided input values. The built-in aggregator determines how to aggregate rule values when you use the COLLECT hit policy.

   
   Note

   The Orientation option is not supported in the Red Hat Process Automation Manager 7.2 DMN designer and will be removed from the Business Central user interface in a future release. This option determines whether rows, columns, or both rows and columns (CrossTable) represent rules in DMN decision tables.

   The following is a more complex decision table that determines applicant qualification for a loan as the concluding decision node in the same loan prequalification decision model:

   Figure 3.10. Decision table for loan prequalification

   

Procedure

1. In Business Central, go to Menu → Design → Projects, click the project name, and select the DMN file you want to modify.
2. In the DMN designer canvas, select a decision node or business knowledge model for which you want to define the data types and click the Edit icon to open the DMN boxed expression designer.

3. If the boxed expression is for a decision node that is not yet defined, double-click the undefined table to select the type of boxed expression you want to use, such as a boxed literal expression, boxed context expression, decision table, or other DMN boxed expression.

   

4. Double-click the cell for the table header, column header, or parameter field (depending on the boxed expression type) for which you want to define the data type and click Manage to create a custom data type.

   

   You can also manage custom data types by selecting the Diagram properties icon in the upper-right corner of the DMN designer:

   

   The data type that you define for a specified cell in a boxed expression determines the structure of the data that you use within that associated table, column, or field in the boxed expression.

   In this example, an output column Credit Score Rating for a DMN decision table defines a set of custom credit score ratings based on an applicant’s credit score.

5. In the Manage Custom Data Types window, click Add and create a Credit_Score_Rating data type as a string with the following constraints: "Excellent","Good","Fair","Poor","Bad".

   

   If the data type requires a list of items, enable the Collection setting.

6. Click Save to save the data type, and in the decision table, double-click the Credit Score Rating column header, set the data type to this new custom data type, and define the rule values for that column with the rating constraints that you specified.

   Figure 3.13. Decision table for credit score rating

   

   In the DMN decision model for this scenario, the Credit Score Rating decision flows into the following Loan Prequalification decision that also requires custom data types:

   
7. Continuing with this example, double-click any column header to return to the Manage Custom Data Types window, click Add, and create a Loan_Qualification data type as a Structure with no constraints.

8. Next to the Loan_Qualification data type, select the settings icon (three vertical dots) and select Insert nested field to insert sub-fields within this parent data type.

   

   You can use these sub-fields in association with the parent structured data type in boxed expressions, such as nested column headers in decision tables or nested table parameters in context or function expressions.

9. For this example, under the structured Loan_Qualification data type, add a Qualification field with constraints "Qualified","Not Qualified" and a Reason field with no constraints. Add also a simple Back_End_Ratio and a Front_End_Ratio data type, both with constraints "Sufficient","Insufficient".

   Click Save for each data type that you create.

   

10. Return to the decision table and, for each column, double-click the column header cell, set the data type to the new corresponding custom data type, and define the rule values as needed for the column with the constraints that you specified, if applicable.

    Figure 3.14. Decision table for loan prequalification