Designing a decision service using DMN models

As a business analyst or business rules developer, you can use Decision Model and Notation (DMN) to model a decision service graphically. The decision requirements of a DMN decision model are determined by a decision requirements graph (DRG) that is depicted in one or more decision requirements diagrams (DRDs). A DRD can represent part or all of the overall DRG for the DMN model. DRDs trace business decisions from start to finish, with each decision node using logic defined in DMN boxed expressions such as decision tables.

IBM Business Automation Manager Open Editions provides runtime support for DMN 1.1, 1.2, 1.3, and 1.4 models at conformance level 3, and design support for DMN 1.2 models at conformance level 3. You can design your DMN models directly in Business Central or with the IBM Business Automation Manager Open Editions DMN modeler in VS Code, or import existing DMN models into your IBM Business Automation Manager Open Editions projects for deployment and execution. Any DMN 1.1 and 1.3 models (do not contain DMN 1.3 features) that you import into Business Central, open in the DMN designer, and save are converted to DMN 1.2 models.

For more information about DMN, see the Object Management Group (OMG) Decision Model and Notation specification.

For a step-by-step tutorial with an example DMN decision service, see Getting started with decision services.

Decision-authoring assets in IBM Business Automation Manager Open Editions

IBM Business Automation Manager Open Editions supports several assets that you can use to define business decisions for your decision service. Each decision-authoring asset has different advantages, and you might prefer to use one or a combination of multiple assets depending on your goals and needs.

The following table highlights the main decision-authoring assets supported in IBM Business Automation Manager Open Editions projects to help you decide or confirm the best method for defining decisions in your decision service.

Table 1. Decision-authoring assets supported in IBM Business Automation Manager Open Editions
Asset	Highlights	Authoring tools	Documentation
Decision Model and Notation (DMN) models	Are decision models based on a notation standard defined by the Object Management Group (OMG) Use graphical decision requirements diagrams (DRDs) that represent part or all of the overall decision requirements graph (DRG) to trace business decision flows Use an XML schema that allows the DMN models to be shared between DMN-compliant platforms Support Friendly Enough Expression Language (FEEL) to define decision logic in DMN decision tables and other DMN boxed expressions Can be integrated efficiently with Business Process Model and Notation (BPMN) process models Are optimal for creating comprehensive, illustrative, and stable decision flows	Business Central or other DMN-compliant editor	Designing a decision service using DMN models
Guided decision tables	Are tables of rules that you create in a UI-based table designer in Business Central Are a wizard-led alternative to spreadsheet decision tables Provide fields and options for acceptable input Support template keys and values for creating rule templates Support hit policies, real-time validation, and other additional features not supported in other assets Are optimal for creating rules in a controlled tabular format to minimize compilation errors	Business Central	Designing a decision service using guided decision tables
Spreadsheet decision tables	Are XLS or XLSX spreadsheet decision tables that you can upload into Business Central Support template keys and values for creating rule templates Are optimal for creating rules in decision tables already managed outside of Business Central Have strict syntax requirements for rules to be compiled properly when uploaded	Spreadsheet editor	Designing a decision service using spreadsheet decision tables
Guided rules	Are individual rules that you create in a UI-based rule designer in Business Central Provide fields and options for acceptable input Are optimal for creating single rules in a controlled format to minimize compilation errors	Business Central	Designing a decision service using guided rules
Guided rule templates	Are reusable rule structures that you create in a UI-based template designer in Business Central Provide fields and options for acceptable input Support template keys and values for creating rule templates (fundamental to the purpose of this asset) Are optimal for creating many rules with the same rule structure but with different defined field values	Business Central	Designing a decision service using guided rule templates
DRL rules	Are individual rules that you define directly in `.drl` text files Provide the most flexibility for defining rules and other technicalities of rule behavior Can be created in certain standalone environments and integrated with IBM Business Automation Manager Open Editions Are optimal for creating rules that require advanced DRL options Have strict syntax requirements for rules to be compiled properly	Business Central or integrated development environment (IDE)	Designing a decision service using DRL rules
Predictive Model Markup Language (PMML) models	Are predictive data-analytic models based on a notation standard defined by the Data Mining Group (DMG) Use an XML schema that allows the PMML models to be shared between PMML-compliant platforms Support Regression, Scorecard, Tree, Mining, and other model types Can be included with a standalone IBM Business Automation Manager Open Editions project or imported into a project in Business Central Are optimal for incorporating predictive data into decision services in IBM Business Automation Manager Open Editions	PMML or XML editor	Designing a decision service using PMML models

When you define business decisions, you can also consider using Red Hat build of Kogito for your cloud-native decision services. For more information about getting started with Red Hat build of Kogito microservices, see Getting started with Red Hat build of Kogito in IBM Business Automation Manager Open Editions.

IBM Business Automation Manager Open Editions BPMN and DMN modelers

IBM Business Automation Manager Open Editions provides the following extensions or applications that you can use to design Business Process Model and Notation (BPMN) process models and Decision Model and Notation (DMN) decision models using graphical modelers.

Business Central: Enables you to view and design BPMN models, DMN models, and test scenario files in a related embedded designer.

To use Business Central, you can set up a development environment containing a Business Central to design business rules and processes, and a KIE Server to execute and test the created business rules and processes.
IBM Business Automation Manager Open Editions VS Code extension: Enables you to view and design BPMN models, DMN models, and test scenario files in Visual Studio Code (VS Code). The VS Code extension requires VS Code 1.46.0 or later.

To install the IBM Business Automation Manager Open Editions VS Code extension, select the Extensions menu option in VS Code and search for and install the KIE Business Automation Bundle extension.
Standalone BPMN and DMN editors: Enable you to view and design BPMN and DMN models embedded in your web applications. To download the necessary files, you can either use the NPM artifacts from the NPM registry or download the JavaScript files directly for the DMN standalone editor library at https://<YOUR_PAGE>/dmn/index.js and for the BPMN standalone editor library at https://<YOUR_PAGE>/bpmn/index.js.

Installing the IBM Business Automation Manager Open Editions VS Code extension bundle

IBM Business Automation Manager Open Editions provides a KIE Business Automation Bundle VS Code extension that enables you to design Decision Model and Notation (DMN) decision models, Business Process Model and Notation (BPMN) 2.0 business processes, and test scenarios directly in VS Code. VS Code is the preferred integrated development environment (IDE) for developing new business applications. IBM Business Automation Manager Open Editions also provides individual DMN Editor and BPMN Editor VS Code extensions for DMN or BPMN support only, if needed.

Important

The editors in the VS Code are partially compatible with the editors in the Business Central, and several Business Central features are not supported in the VS Code.

Prerequisites

The latest stable version of VS Code is installed.

Procedure

In your VS Code IDE, select the Extensions menu option and search for KIE Business Automation Bundle for DMN, BPMN, and test scenario file support.

For DMN or BPMN file support only, you can also search for the individual DMN Editor or BPMN Editor extensions.
When the KIE Business Automation Bundle extension appears in VS Code, select it and click Install.
For optimal VS Code editor behavior, after the extension installation is complete, reload or close and re-launch your instance of VS Code.

After you install the VS Code extension bundle, any .dmn, .bpmn, or .bpmn2 files that you open or create in VS Code are automatically displayed as graphical models. Additionally, any .scesim files that you open or create are automatically displayed as tabular test scenario models for testing the functionality of your business decisions.

If the DMN, BPMN, or test scenario modelers open only the XML source of a DMN, BPMN, or test scenario file and displays an error message, review the reported errors and the model file to ensure that all elements are correctly defined.

Note

For new DMN or BPMN models, you can also enter dmn.new or bpmn.new in a web browser to design your DMN or BPMN model in the online modeler. When you finish creating your model, you can click Download in the online modeler page to import your DMN or BPMN file into your IBM Business Automation Manager Open Editions project in VS Code.

Configuring the IBM Business Automation Manager Open Editions standalone editors

IBM Business Automation Manager Open Editions provides standalone editors that are distributed in a self-contained library providing an all-in-one JavaScript file for each editor. The JavaScript file uses a comprehensive API to set and control the editor.

You can install the standalone editors using the following methods:

Download each JavaScript file manually
Use the NPM package

Procedure

Install the standalone editors using one of the following methods:
Download each JavaScript file manually: For this method, follow these steps:
1. Download the JavaScript files.
2. Add the downloaded Javascript files to your hosted application.
3. Add the following <script> tag to your HTML page:
  
  Script tag for your HTML page for the DMN editor
  
  <script src="https://<YOUR_PAGE>/dmn/index.js"></script>
  
  Script tag for your HTML page for the BPMN editor
  
  <script src="https://<YOUR_PAGE>/bpmn/index.js"></script>
Use the NPM package: For this method, follow these steps:
1. Add the NPM package to your package.json file:
  
  Adding the NPM package
  
  npm install @kie-tools/kie-editors-standalone
2. Import each editor library to your TypeScript file:
  
  Importing each editor
  
  import * as DmnEditor from "@kie-tools/kie-editors-standalone/dist/dmn" import * as BpmnEditor from "@kie-tools/kie-editors-standalone/dist/bpmn"

After you install the standalone editors, open the required editor by using the provided editor API, as shown in the following example for opening a DMN editor. The API is the same for each editor.

Opening the DMN standalone editor

const editor = DmnEditor.open({
  container: document.getElementById("dmn-editor-container"),
  initialContent: Promise.resolve(""),
  readOnly: false,
  origin: "",
  resources: new Map([
    [
      "MyIncludedModel.dmn",
      {
        contentType: "text",
        content: Promise.resolve("")
      }
    ]
  ])
});

Use the following parameters with the editor API:

Table 2. Example parameters
Parameter	Description
`container`	HTML element in which the editor is appended.
`initialContent`	Promise to a DMN model content. This parameter can be empty, as shown in the following examples: `Promise.resolve("")` `Promise.resolve("<DIAGRAM_CONTENT_DIRECTLY_HERE>")` `fetch("MyDmnModel.dmn").then(content ⇒ content.text())`
`readOnly` (Optional)	Enables you to allow changes in the editor. Set to `false` (default) to allow content editing and `true` for read-only mode in editor.
`origin` (Optional)	Origin of the repository. The default value is `window.location.origin`.
`resources` (Optional)	Map of resources for the editor. For example, this parameter is used to provide included models for the DMN editor or work item definitions for the BPMN editor. Each entry in the map contains a resource name and an object that consists of `content-type` (`text` or `binary`) and `content` (similar to the `initialContent` parameter).

The returned object contains the methods that are required to manipulate the editor.

Table 3. Returned object methods
Method	Description
`getContent(): Promise<string>`	Returns a promise containing the editor content.
`setContent(path: string, content: string): void`	Sets the content of the editor.
`getPreview(): Promise<string>`	Returns a promise containing an SVG string of the current diagram.
`subscribeToContentChanges(callback: (isDirty: boolean) ⇒ void): (isDirty: boolean) ⇒ void`	Sets a callback to be called when the content changes in the editor and returns the same callback to be used for unsubscription.
`unsubscribeToContentChanges(callback: (isDirty: boolean) ⇒ void): void`	Unsubscribes the passed callback when the content changes in the editor.
`markAsSaved(): void`	Resets the editor state that indicates that the content in the editor is saved. Also, it activates the subscribed callbacks related to content change.
`undo(): void`	Undoes the last change in the editor. Also, it activates the subscribed callbacks related to content change.
`redo(): void`	Redoes the last undone change in the editor. Also, it activates the subscribed callbacks related to content change.
`close(): void`	Closes the editor.
`getElementPosition(selector: string): Promise<Rect>`	Provides an alternative to extend the standard query selector when an element lives inside a canvas or a video component. The `selector` parameter must follow the `<PROVIDER>:::<SELECT>` format, such as `Canvas:::MySquare` or `Video:::PresenterHand`. This method returns a `Rect` representing the element position.
`envelopeApi: MessageBusClientApi<KogitoEditorEnvelopeApi>`	This is an advanced editor API. For more information about advanced editor API, see MessageBusClientApi and KogitoEditorEnvelopeApi.

Creating and executing DMN and BPMN models using Maven

You can use Maven archetypes to develop DMN and BPMN models in VS Code using the IBM Business Automation Manager Open Editions VS Code extension instead of Business Central. You can then integrate your archetypes with your IBM Business Automation Manager Open Editions decision and process services in Business Central as needed. This method of developing DMN and BPMN models is helpful for building new business applications using the IBM Business Automation Manager Open Editions VS Code extension.

Procedure

In a command terminal, navigate to a local folder where you want to store the new IBM Business Automation Manager Open Editions project.
Enter the following command to use a Maven archtype to generate a project within a defined folder:
Generating a project using Maven archetype
```
mvn archetype:generate \
    -DarchetypeGroupId=org.kie \
    -DarchetypeArtifactId=kie-kjar-archetype \
    -DarchetypeVersion=7.67.2.Final-redhat-0017
```
This command generates a Maven project with required dependencies and generates required directories and files to build your business application. You can use the Git version control system (recommended) when developing a project.

If you want to generate multiple projects in the same directory, specify the artifactId and groupId of the generated business application by adding -DgroupId=<groupid> -DartifactId=<artifactId> to the previous command.
In your VS Code IDE, click File, select Open Folder, and navigate to the folder that is generated using the previous command.
Before creating the first asset, set a package for your business application, for example, org.kie.businessapp, and create respective directories in the following paths:
- PROJECT_HOME/src/main/java
- PROJECT_HOME/src/main/resources
- PROJECT_HOME/src/test/resources
For example, you can create PROJECT_HOME/src/main/java/org/kie/businessapp for org.kie.businessapp package.
Use VS Code to create assets for your business application. You can create the assets supported by IBM Business Automation Manager Open Editions VS Code extension using the following ways:
- To create a business process, create a new file with .bpmn or .bpmn2 in PROJECT_HOME/src/main/resources/org/kie/businessapp directory, such as Process.bpmn.
- To create a DMN model, create a new file with .dmn in PROJECT_HOME/src/main/resources/org/kie/businessapp directory, such as AgeDecision.dmn.
- To create a test scenario simulation model, create a new file with .scesim in PROJECT_HOME/src/test/resources/org/kie/businessapp directory, such as TestAgeScenario.scesim.
After you create the assets in your Maven archetype, navigate to the root directory (contains pom.xml) of the project in the command line and run the following command to build the knowledge JAR (KJAR) of your project:
```
mvn clean install
```
If the build fails, address any problems described in the command line error messages and try again to validate the project until the build is successful. However, if the build is successful, you can find the artifact of your business application in PROJECT_HOME/target directory.

Note
Use mvn clean install command often to validate your project after each major change during development.

You can deploy the generated knowledge JAR (KJAR) of your business application on a running KIE Server using the REST API. For more information about using REST API, see Interacting with IBM Business Automation Manager Open Editions using KIE APIs.

Decision Model and Notation (DMN)

Decision Model and Notation (DMN) is a standard established by the Object Management Group (OMG) for describing and modeling operational decisions. DMN defines an XML schema that enables DMN models to be shared between DMN-compliant platforms and across organizations so that business analysts and business rules developers can collaborate in designing and implementing DMN decision services. The DMN standard is similar to and can be used together with the Business Process Model and Notation (BPMN) standard for designing and modeling business processes.

For more information about the background and applications of DMN, see the OMG Decision Model and Notation specification.

DMN conformance levels

The DMN specification defines three incremental levels of conformance in a software implementation. A product that claims compliance at one level must also be compliant with any preceding levels. For example, a conformance level 3 implementation must also include the supported components in conformance levels 1 and 2. For the formal definitions of each conformance level, see the OMG Decision Model and Notation specification.

The following list summarizes the three DMN conformance levels:

Conformance level 1: A DMN conformance level 1 implementation supports decision requirement diagrams (DRDs), decision logic, and decision tables, but decision models are not executable. Any language can be used to define the expressions, including natural, unstructured languages.
Conformance level 2: A DMN conformance level 2 implementation includes the requirements in conformance level 1, and supports Simplified Friendly Enough Expression Language (S-FEEL) expressions and fully executable decision models.
Conformance level 3: A DMN conformance level 3 implementation includes the requirements in conformance levels 1 and 2, and supports Friendly Enough Expression Language (FEEL) expressions, the full set of boxed expressions, and fully executable decision models.

DMN decision requirements diagram (DRD) components

A decision requirements diagram (DRD) is a visual representation of your DMN model. A DRD can represent part or all of the overall decision requirements graph (DRG) for the DMN model. DRDs trace business decisions using decision nodes, business knowledge models, sources of business knowledge, input data, and decision services.

The following table summarizes the components in a DRD:

Table 4. DRD components
Component		Description
Elements	Decision	Node where one or more input elements determine an output based on defined decision logic.
	Business knowledge model	Reusable function with one or more decision elements. Decisions that have the same logic but depend on different sub-input data or sub-decisions use business knowledge models to determine which procedure to follow.
	Knowledge source	External authorities, documents, committees, or policies that regulate a decision or business knowledge model. Knowledge sources are references to real-world factors rather than executable business rules.
	Input data	Information used in a decision node or a business knowledge model. Input data usually includes business-level concepts or objects relevant to the business, such as loan applicant data used in a lending strategy.
	Decision service	Top-level decision containing a set of reusable decisions published as a service for invocation. A decision service can be invoked from an external application or a BPMN business process.
Requirement connectors	Information requirement	Connection from an input data node or decision node to another decision node that requires the information.
	Knowledge requirement	Connection from a business knowledge model to a decision node or to another business knowledge model that invokes the decision logic.
	Authority requirement	Connection from an input data node or a decision node to a dependent knowledge source or from a knowledge source to a decision node, business knowledge model, or another knowledge source.
Artifacts	Text annotation	Explanatory note associated with an input data node, decision node, business knowledge model, or knowledge source.
Artifacts	Association	Connection from an input data node, decision node, business knowledge model, or knowledge source to a text annotation.

The following table summarizes the permitted connectors between DRD elements:

Table 5. DRD connector rules
Starts from	Connects to	Connection type
Decision	Decision	Information requirement
Business knowledge model	Decision	Knowledge requirement
Business knowledge model	Business knowledge model	Knowledge requirement
Decision service	Decision	Knowledge requirement
Decision service	Business knowledge model	Knowledge requirement
Input data	Decision	Information requirement
Input data	Knowledge source	Authority requirement
Knowledge source	Decision	Authority requirement
	Business knowledge model
	Knowledge source
Decision	Text annotation	Association
Business knowledge model
Knowledge source
Input data

The following example DRD illustrates some of these DMN components in practice:

Figure 1. Example DRD: Loan prequalification

The following example DRD illustrates DMN components that are part of a reusable decision service:

Figure 2. Example DRD: Phone call handling as a decision service

In a DMN decision service node, the decision nodes in the bottom segment incorporate input data from outside of the decision service to arrive at a final decision in the top segment of the decision service node. The resulting top-level decisions from the decision service are then implemented in any subsequent decisions or business knowledge requirements of the DMN model. You can reuse DMN decision services in other DMN models to apply the same decision logic with different input data and different outgoing connections.

Rule expressions in FEEL

Friendly Enough Expression Language (FEEL) is an expression language defined by the Object Management Group (OMG) DMN specification. FEEL expressions define the logic of a decision in a DMN model. FEEL is designed to facilitate both decision modeling and execution by assigning semantics to the decision model constructs. FEEL expressions in decision requirements diagrams (DRDs) occupy table cells in boxed expressions for decision nodes and business knowledge models.

For more information about FEEL in DMN, see the OMG Decision Model and Notation specification.

Data types in FEEL

Friendly Enough Expression Language (FEEL) supports the following data types:

Numbers
Strings
Boolean values
Dates
Time
Date and time
Days and time duration
Years and months duration
Functions
Contexts
Ranges (or intervals)
Lists

Note	The DMN specification currently does not provide an explicit way of declaring a variable as a `function`, `context`, `range`, or `list`, but IBM Business Automation Manager Open Editions extends the DMN built-in types to support variables of these types.

The following list describes each data type:

Numbers

Numbers in FEEL are based on the IEEE 754-2008 Decimal 128 format, with 34 digits of precision. Internally, numbers are represented in Java as BigDecimals with MathContext DECIMAL128. FEEL supports only one number data type, so the same type is used to represent both integers and floating point numbers.

FEEL numbers use a dot (.) as a decimal separator. FEEL does not support -INF, +INF, or NaN. FEEL uses null to represent invalid numbers.

IBM Business Automation Manager Open Editions extends the DMN specification and supports additional number notations:

Scientific: You can use scientific notation with the suffix e<exp> or E<exp>. For example, 1.2e3 is the same as writing the expression 1.2*10**3, but is a literal instead of an expression.
Hexadecimal: You can use hexadecimal numbers with the prefix 0x. For example, 0xff is the same as the decimal number 255. Both uppercase and lowercase letters are supported. For example, 0XFF is the same as 0xff.
Type suffixes: You can use the type suffixes f, F, d, D, l, and L. These suffixes are ignored.

Strings

Strings in FEEL are any sequence of characters delimited by double quotation marks.

Example

"John Doe"

Boolean values

FEEL uses three-valued boolean logic, so a boolean logic expression may have values true, false, or null.

Dates

Date literals are not supported in FEEL, but you can use the built-in date() function to construct date values. Date strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document. The format is "YYYY-MM-DD" where YYYY is the year with four digits, MM is the number of the month with two digits, and DD is the number of the day.

Example:

date( "2017-06-23" )

Date objects have time equal to "00:00:00", which is midnight. The dates are considered to be local, without a timezone.

Time

Time literals are not supported in FEEL, but you can use the built-in time() function to construct time values. Time strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document. The format is "hh:mm:ss[.uuu][(+-)hh:mm]" where hh is the hour of the day (from 00 to 23), mm is the minutes in the hour, and ss is the number of seconds in the minute. Optionally, the string may define the number of milliseconds (uuu) within the second and contain a positive (+) or negative (-) offset from UTC time to define its timezone. Instead of using an offset, you can use the letter z to represent the UTC time, which is the same as an offset of -00:00. If no offset is defined, the time is considered to be local.

Examples:

time( "04:25:12" )
time( "14:10:00+02:00" )
time( "22:35:40.345-05:00" )
time( "15:00:30z" )

Time values that define an offset or a timezone cannot be compared to local times that do not define an offset or a timezone.

Date and time

Date and time literals are not supported in FEEL, but you can use the built-in date and time() function to construct date and time values. Date and time strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document. The format is "<date>T<time>", where <date> and <time> follow the prescribed XML schema formatting, conjoined by T.

Examples:

date and time( "2017-10-22T23:59:00" )
date and time( "2017-06-13T14:10:00+02:00" )
date and time( "2017-02-05T22:35:40.345-05:00" )
date and time( "2017-06-13T15:00:30z" )

Date and time values that define an offset or a timezone cannot be compared to local date and time values that do not define an offset or a timezone.

Important

If your implementation of the DMN specification does not support spaces in the XML schema, use the keyword dateTime as a synonym of date and time.

Days and time duration

Days and time duration literals are not supported in FEEL, but you can use the built-in duration() function to construct days and time duration values. Days and time duration strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document, but are restricted to only days, hours, minutes and seconds. Months and years are not supported.

Examples:

duration( "P1DT23H12M30S" )
duration( "P23D" )
duration( "PT12H" )
duration( "PT35M" )

Important

If your implementation of the DMN specification does not support spaces in the XML schema, use the keyword dayTimeDuration as a synonym of days and time duration.

Years and months duration

Years and months duration literals are not supported in FEEL, but you can use the built-in duration() function to construct days and time duration values. Years and months duration strings in FEEL follow the format defined in the XML Schema Part 2: Datatypes document, but are restricted to only years and months. Days, hours, minutes, or seconds are not supported.

Examples:

duration( "P3Y5M" )
duration( "P2Y" )
duration( "P10M" )
duration( "P25M" )

Important

If your implementation of the DMN specification does not support spaces in the XML schema, use the keyword yearMonthDuration as a synonym of years and months duration.

Functions

FEEL has function literals (or anonymous functions) that you can use to create functions. The DMN specification currently does not provide an explicit way of declaring a variable as a function, but IBM Business Automation Manager Open Editions extends the DMN built-in types to support variables of functions.

Example:

function(a, b) a + b

In this example, the FEEL expression creates a function that adds the parameters a and b and returns the result.

Contexts

FEEL has context literals that you can use to create contexts. A context in FEEL is a list of key and value pairs, similar to maps in languages like Java. The DMN specification currently does not provide an explicit way of declaring a variable as a context, but IBM Business Automation Manager Open Editions extends the DMN built-in types to support variables of contexts.

Example:

{ x : 5, y : 3 }

In this example, the expression creates a context with two entries, x and y, representing a coordinate in a chart.

In DMN 1.2, another way to create contexts is to create an item definition that contains the list of keys as attributes, and then declare the variable as having that item definition type.

The IBM Business Automation Manager Open Editions DMN API supports DMN ItemDefinition structural types in a DMNContext represented in two ways:

User-defined Java type: Must be a valid JavaBeans object defining properties and getters for each of the components in the DMN ItemDefinition. If necessary, you can also use the @FEELProperty annotation for those getters representing a component name which would result in an invalid Java identifier.
java.util.Map interface: The map needs to define the appropriate entries, with the keys corresponding to the component name in the DMN ItemDefinition.

Ranges (or intervals)

FEEL has range literals that you can use to create ranges or intervals. A range in FEEL is a value that defines a lower and an upper bound, where either can be open or closed. The DMN specification currently does not provide an explicit way of declaring a variable as a range, but IBM Business Automation Manager Open Editions extends the DMN built-in types to support variables of ranges.

The syntax of a range is defined in the following formats:

range          := interval_start endpoint '..' endpoint interval_end
interval_start := open_start | closed_start
open_start     := '(' | ']'
closed_start   := '['
interval_end   := open_end | closed_end
open_end       := ')' | '['
closed_end     := ']'
endpoint       := expression

The expression for the endpoint must return a comparable value, and the lower bound endpoint must be lower than the upper bound endpoint.

For example, the following literal expression defines an interval between 1 and 10, including the boundaries (a closed interval on both endpoints):

[ 1 .. 10 ]

The following literal expression defines an interval between 1 hour and 12 hours, including the lower boundary (a closed interval), but excluding the upper boundary (an open interval):

[ duration("PT1H") .. duration("PT12H") )

You can use ranges in decision tables to test for ranges of values, or use ranges in simple literal expressions. For example, the following literal expression returns true if the value of a variable x is between 0 and 100:

x in [ 1 .. 100 ]

Lists

FEEL has list literals that you can use to create lists of items. A list in FEEL is represented by a comma-separated list of values enclosed in square brackets. The DMN specification currently does not provide an explicit way of declaring a variable as a list, but IBM Business Automation Manager Open Editions extends the DMN built-in types to support variables of lists.

Example:

[ 2, 3, 4, 5 ]

All lists in FEEL contain elements of the same type and are immutable. Elements in a list can be accessed by index, where the first element is 1. Negative indexes can access elements starting from the end of the list so that -1 is the last element.

For example, the following expression returns the second element of a list x:

x[2]

The following expression returns the second-to-last element of a list x:

x[-2]

Elements in a list can also be counted by the function count, which uses the list of elements as the parameter.

For example, the following expression returns 4:

count([ 2, 3, 4, 5 ])

Built-in functions in FEEL

To promote interoperability with other platforms and systems, Friendly Enough Expression Language (FEEL) includes a library of built-in functions. The built-in FEEL functions are implemented in the Drools Decision Model and Notation (DMN) engine so that you can use the functions in your DMN decision services.

The following sections describe each built-in FEEL function, listed in the format NAME( PARAMETERS ). For more information about FEEL functions in DMN, see the OMG Decision Model and Notation specification.

Conversion functions

The following functions support conversion between values of different types. Some of these functions use specific string formats, such as the following examples:

date string: Follows the format defined in the XML Schema Part 2: Datatypes document, such as 2020-06-01
time string: Follows one of the following formats:
- Format defined in the XML Schema Part 2: Datatypes document, such as 23:59:00z
- Format for a local time defined by ISO 8601 followed by @ and an IANA Timezone, such as 00:01:00@Etc/UTC
date time string: Follows the format of a date string followed by T and a time string, such as 2012-12-25T11:00:00Z
duration string: Follows the format of days and time duration and years and months duration defined in the XQuery 1.0 and XPath 2.0 Data Model, such as P1Y2M

date( from ) - using date

Converts from to a date value.

Table 6. Parameters
Parameter	Type	Format
`from`	`string`	`date string`

Example

date( "2012-12-25" ) - date( "2012-12-24" ) = duration( "P1D" )

date( from ) - using date and time

Converts from to a date value and sets time components to null.

Table 7. Parameters
Parameter	Type
`from`	`date and time`

Example

date(date and time( "2012-12-25T11:00:00Z" )) = date( "2012-12-25" )

date( year, month, day )

Produces a date from the specified year, month, and day values.

Table 8. Parameters
Parameter	Type
`year`	`number`
`month`	`number`
`day`	`number`

Example

date( 2012, 12, 25 ) = date( "2012-12-25" )

date and time( date, time )

Produces a date and time from the specified date and ignores any time components and the specified time.

Table 9. Parameters
Parameter	Type
`date`	`date` or `date and time`
`time`	`time`

Example

date and time ( "2012-12-24T23:59:00" ) = date and time(date( "2012-12-24" ), time( "23:59:00" ))

date and time( from )

Produces a date and time from the specified string.

Table 10. Parameters
Parameter	Type	Format
`from`	`string`	`date time string`

Example

date and time( "2012-12-24T23:59:00" ) + duration( "PT1M" ) = date and time( "2012-12-25T00:00:00" )

time( from )

Produces a time from the specified string.

Table 11. Parameters
Parameter	Type	Format
`from`	`string`	`time string`

Example

time( "23:59:00z" ) + duration( "PT2M" ) = time( "00:01:00@Etc/UTC" )

time( from )

Produces a time from the specified parameter and ignores any date components.

Table 12. Parameters
Parameter	Type
`from`	`time` or `date and time`

Example

time(date and time( "2012-12-25T11:00:00Z" )) = time( "11:00:00Z" )

time( hour, minute, second, offset? )

Produces a time from the specified hour, minute, and second component values.

Table 13. Parameters
Parameter	Type
`hour`	`number`
`minute`	`number`
`second`	`number`
`offset` (Optional)	`days and time duration` or null

Example

time( "23:59:00z" ) = time(23, 59, 0, duration( "PT0H" ))

number( from, grouping separator, decimal separator )

Converts from to a number using the specified separators.

Table 14. Parameters
Parameter	Type
`from`	`string` representing a valid number
`grouping separator`	Space ( ), comma (`,`), period (`.`), or null
`decimal separator`	Same types as `grouping separator`, but the values cannot match

Example

number( "1 000,0", " ", "," ) = number( "1,000.0", ",", "." )

string( from )

Provides a string representation of the specified parameter.

Table 15. Parameters
Parameter	Type
`from`	Non-null value

Examples

string( 1.1 ) = "1.1"
string( null ) = null

duration( from )

Converts from to a days and time duration value or years and months duration value.

Table 16. Parameters
Parameter	Type	Format
`from`	`string`	`duration string`

Examples

date and time( "2012-12-24T23:59:00" ) - date and time( "2012-12-22T03:45:00" ) = duration( "P2DT20H14M" )
duration( "P2Y2M" ) = duration( "P26M" )

years and months duration( from, to )

Calculates the years and months duration between the two specified parameters.

Table 17. Parameters
Parameter	Type
`from`	`date` or `date and time`
`to`	`date` or `date and time`

Example

years and months duration( date( "2011-12-22" ), date( "2013-08-24" ) ) = duration( "P1Y8M" )

Boolean functions

The following functions support Boolean operations.

not( negand )

Performs the logical negation of the negand operand.

Table 18. Parameters
Parameter	Type
`negand`	`boolean`

Examples

not( true ) = false
not( null ) = null

String functions

The following functions support string operations.

Note	In FEEL, Unicode characters are counted based on their code points.

substring( string, start position, length? )

Returns the substring from the start position for the specified length. The first character is at position value 1.

Table 19. Parameters
Parameter	Type
`string`	`string`
`start position`	`number`
`length` (Optional)	`number`

Examples

substring( "testing",3 ) = "sting"
substring( "testing",3,3 ) = "sti"
substring( "testing", -2, 1 ) = "n"
substring( "\U01F40Eab", 2 ) = "ab"

Note	In FEEL, the string literal `"\U01F40Eab"` is the `🐎ab` string (horse symbol followed by `a` and `b`).

string length( string )

Calculates the length of the specified string.

Table 20. Parameters
Parameter	Type
`string`	`string`

Examples

string length( "tes" ) = 3
string length( "\U01F40Eab" ) = 3

upper case( string )

Produces an uppercase version of the specified string.

Table 21. Parameters
Parameter	Type
`string`	`string`

Example

upper case( "aBc4" ) = "ABC4"

lower case( string )

Produces a lowercase version of the specified string.

Table 22. Parameters
Parameter	Type
`string`	`string`

Example

lower case( "aBc4" ) = "abc4"

substring before( string, match )

Calculates the substring before the match.

Table 23. Parameters
Parameter	Type
`string`	`string`
`match`	`string`

Examples

substring before( "testing", "ing" ) = "test"
substring before( "testing", "xyz" ) = ""

substring after( string, match )

Calculates the substring after the match.

Table 24. Parameters
Parameter	Type
`string`	`string`
`match`	`string`

Examples

substring after( "testing", "test" ) = "ing"
substring after( "", "a" ) = ""

replace( input, pattern, replacement, flags? )

Calculates the regular expression replacement.

Table 25. Parameters
Parameter	Type
`input`	`string`
`pattern`	`string`
`replacement`	`string`
`flags` (Optional)	`string`

Note	This function uses regular expression parameters as defined in XQuery 1.0 and XPath 2.0 Functions and Operators.

Example

replace( "abcd", "(ab)|(a)", "[1=$1][2=$2]" ) = "[1=ab][2=]cd"

contains( string, match )

Returns true if the string contains the match.

Table 26. Parameters
Parameter	Type
`string`	`string`
`match`	`string`

Example

contains( "testing", "to" ) = false

starts with( string, match )

Returns true if the string starts with the match

Table 27. Parameters
Parameter	Type
`string`	`string`
`match`	`string`

Example

starts with( "testing", "te" ) = true

ends with( string, match )

Returns true if the string ends with the match.

Table 28. Parameters
Parameter	Type
`string`	`string`
`match`	`string`

Example

ends with( "testing", "g" ) = true

matches( input, pattern, flags? )

Returns true if the input matches the regular expression.

Table 29. Parameters
Parameter	Type
`input`	`string`
`pattern`	`string`
`flags` (Optional)	`string`

Note	This function uses regular expression parameters as defined in XQuery 1.0 and XPath 2.0 Functions and Operators.

Example

matches( "teeesting", "^te*sting" ) = true

split( string, delimiter )

Returns a list of the original string and splits it at the delimiter regular expression pattern.

Table 30. Parameters
Parameter	Type
`string`	`string`
`delimiter`	`string` for a regular expression pattern

Note	This function uses regular expression parameters as defined in XQuery 1.0 and XPath 2.0 Functions and Operators.

Examples

split( "John Doe", "\\s" ) = ["John", "Doe"]
split( "a;b;c;;", ";" ) = ["a","b","c","",""]

List functions

The following functions support list operations.

Note	In FEEL, the index of the first element in a list is `1`. The index of the last element in a list can be identified as `-1`.

list contains( list, element )

Returns true if the list contains the element.

Table 31. Parameters
Parameter	Type
`list`	`list`
`element`	Any type, including null

Example

list contains( [1,2,3], 2 ) = true

count( list )

Counts the elements in the list.

Table 32. Parameters
Parameter	Type
`list`	`list`

Examples

count( [1,2,3] ) = 3
count( [] ) = 0
count( [1,[2,3]] ) = 2

min( list )

Returns the minimum comparable element in the list.

Table 33. Parameters
Parameter	Type
`list`	`list`

Alternative signature

min( e1, e2, ..., eN )

Examples

min( [1,2,3] ) = 1
min( 1 ) = 1
min( [1] ) = 1

max( list )

Returns the maximum comparable element in the list.

Table 34. Parameters
Parameter	Type
`list`	`list`

Alternative signature

max( e1, e2, ..., eN )

Examples

max( 1,2,3 ) = 3
max( [] ) = null

sum( list )

Returns the sum of the numbers in the list.

Table 35. Parameters
Parameter	Type
`list`	`list` of `number` elements

Alternative signature

sum( n1, n2, ..., nN )

Examples

sum( [1,2,3] ) = 6
sum( 1,2,3 ) = 6
sum( 1 ) = 1
sum( [] ) = null

mean( list )

Calculates the average (arithmetic mean) of the elements in the list.

Table 36. Parameters
Parameter	Type
`list`	`list` of `number` elements

Alternative signature

mean( n1, n2, ..., nN )

Examples

mean( [1,2,3] ) = 2
mean( 1,2,3 ) = 2
mean( 1 ) = 1
mean( [] ) = null

all( list )

Returns true if all elements in the list are true.

Table 37. Parameters
Parameter	Type
`list`	`list` of `boolean` elements

Alternative signature

all( b1, b2, ..., bN )

Examples

all( [false,null,true] ) = false
all( true ) = true
all( [true] ) = true
all( [] ) = true
all( 0 ) = null

any( list )

Returns true if any element in the list is true.

Table 38. Parameters
Parameter	Type
`list`	`list` of `boolean` elements

Alternative signature

any( b1, b2, ..., bN )

Examples

any( [false,null,true] ) = true
any( false ) = false
any( [] ) = false
any( 0 ) = null

sublist( list, start position, length? )

Returns the sublist from the start position, limited to the length elements.

Table 39. Parameters
Parameter	Type
`list`	`list`
`start position`	`number`
`length` (Optional)	`number`

Example

sublist( [4,5,6], 1, 2 ) = [4,5]

append( list, item )

Creates a list that is appended to the item or items.

Table 40. Parameters
Parameter	Type
`list`	`list`
`item`	Any type

Example

append( [1], 2, 3 ) = [1,2,3]

concatenate( list )

Creates a list that is the result of the concatenated lists.

Table 41. Parameters
Parameter	Type
`list`	`list`

Example

concatenate( [1,2],[3] ) = [1,2,3]

insert before( list, position, newItem )

Creates a list with the newItem inserted at the specified position.

Table 42. Parameters
Parameter	Type
`list`	`list`
`position`	`number`
`newItem`	Any type

Example

insert before( [1,3],1,2 ) = [2,1,3]

remove( list, position )

Creates a list with the removed element excluded from the specified position.

Table 43. Parameters
Parameter	Type
`list`	`list`
`position`	`number`

Example

remove( [1,2,3], 2 ) = [1,3]

reverse( list )

Returns a reversed list.

Table 44. Parameters
Parameter	Type
`list`	`list`

Example

reverse( [1,2,3] ) = [3,2,1]

index of( list, match )

Returns indexes matching the element.

Parameters

list of type list
match of any type

Table 45. Parameters
Parameter	Type
`list`	`list`
`match`	Any type

Example

index of( [1,2,3,2],2 ) = [2,4]

union( list )

Returns a list of all the elements from multiple lists and excludes duplicates.

Table 46. Parameters
Parameter	Type
`list`	`list`

Example

union( [1,2],[2,3] ) = [1,2,3]

distinct values( list )

Returns a list of elements from a single list and excludes duplicates.

Table 47. Parameters
Parameter	Type
`list`	`list`

Example

distinct values( [1,2,3,2,1] ) = [1,2,3]

flatten( list )

Returns a flattened list.

Table 48. Parameters
Parameter	Type
`list`	`list`

Example

flatten( [[1,2],[[3]], 4] ) = [1,2,3,4]

product( list )

Returns the product of the numbers in the list.

Table 49. Parameters
Parameter	Type
`list`	`list` of `number` elements

Alternative signature

product( n1, n2, ..., nN )

Examples

product( [2, 3, 4] ) = 24
product( 2, 3, 4 ) = 24

median( list )

Returns the median of the numbers in the list. If the number of elements is odd, the result is the middle element. If the number of elements is even, the result is the average of the two middle elements.

Table 50. Parameters
Parameter	Type
`list`	`list` of `number` elements

Alternative signature

median( n1, n2, ..., nN )

Examples

median( 8, 2, 5, 3, 4 ) = 4
median( [6, 1, 2, 3] ) = 2.5
median( [ ] ) = null

stddev( list )

Returns the standard deviation of the numbers in the list.

Table 51. Parameters
Parameter	Type
`list`	`list` of `number` elements

Alternative signature

stddev( n1, n2, ..., nN )

Examples

stddev( 2, 4, 7, 5 ) = 2.081665999466132735282297706979931
stddev( [47] ) = null
stddev( 47 ) = null
stddev( [ ] ) = null

mode( list )

Returns the mode of the numbers in the list. If multiple elements are returned, the numbers are sorted in ascending order.

Table 52. Parameters
Parameter	Type
`list`	`list` of `number` elements

Alternative signature

mode( n1, n2, ..., nN )

Examples

mode( 6, 3, 9, 6, 6 ) = [6]
mode( [6, 1, 9, 6, 1] ) = [1, 6]
mode( [ ] ) = [ ]

Loop statements

Loop statements can transform lists or verify if some elements satisfy a specific condition:

for in (list)

Iterates the elements of the list.

Table 53. Parameters
Parameter	Type
`list`	`list` of `Any` elements

Examples

for i in [1, 2, 3] return i * i = [1, 4, 9]
for i in [1,2,3], j in [1,2,3] return i*j = [1, 2, 3, 2, 4, 6, 3, 6, 9]

some in (list) satisfies (condition)

Returns to single boolean value (true or false), if any element in the list satisfies the condition.

Table 54. Parameters
Parameter	Type
`list`	`list` of `Any` elements
`condition`	boolean expression evaluated to true or false

Examples

some i in [1, 2, 3] satisfies i > 3 = true
some i in [1, 2, 3] satisfies i > 4 = false

every in (list) satisfies (condition)

Returns to single boolean value (true or false), if every element in the list satisfies the condition.

Table 55. Parameters
Parameter	Type
`list`	`list` of `Any` elements
`condition`	boolean expression evaluated to true or false

Examples

every i in [1, 2, 3] satisfies i > 1 = false
every i in [1, 2, 3] satisfies i > 0 = true

Numeric functions

The following functions support number operations.

decimal( n, scale )

Returns a number with the specified scale.

Table 56. Parameters
Parameter	Type
`n`	`number`
`scale`	`number` in the range `[−6111..6176]`

Note	This function is implemented to be consistent with the `FEEL:number` definition for rounding decimal numbers to the nearest even decimal number.

Examples

decimal( 1/3, 2 ) = .33
decimal( 1.5, 0 ) = 2
decimal( 2.5, 0 ) = 2
decimal( 1.035, 2 ) = 1.04
decimal( 1.045, 2 ) = 1.04
decimal( 1.055, 2 ) = 1.06
decimal( 1.065, 2 ) = 1.06

floor( n )

Returns the greatest integer that is less than or equal to the specified number.

Table 57. Parameters
Parameter	Type
`n`	`number`

Examples

floor( 1.5 ) = 1
floor( -1.5 ) = -2

ceiling( n )

Returns the smallest integer that is greater than or equal to the specified number.

Table 58. Parameters
Parameter	Type
`n`	`number`

Examples

ceiling( 1.5 ) = 2
ceiling( -1.5 ) = -1

abs( n )

Returns the absolute value.

Table 59. Parameters
Parameter	Type
`n`	`number`, `days and time duration`, or `years and months duration`

Examples

abs( 10 ) = 10
abs( -10 ) = 10
abs( @"PT5H" ) = @"PT5H"
abs( @"-PT5H" ) = @"PT5H"

modulo( dividend, divisor )

Returns the remainder of the division of the dividend by the divisor. If either the dividend or divisor is negative, the result is of the same sign as the divisor.

Note	This function is also expressed as `modulo(dividend, divisor) = dividend - divisor*floor(dividen d/divisor)`.

Table 60. Parameters
Parameter	Type
`dividend`	`number`
`divisor`	`number`

Examples

modulo( 12, 5 ) = 2
modulo( -12,5 )= 3
modulo( 12,-5 )= -3
modulo( -12,-5 )= -2
modulo( 10.1, 4.5 )= 1.1
modulo( -10.1, 4.5 )= 3.4
modulo( 10.1, -4.5 )= -3.4
modulo( -10.1, -4.5 )= -1.1

sqrt( number )

Returns the square root of the specified number.

Table 61. Parameters
Parameter	Type
`n`	`number`

Example

sqrt( 16 ) = 4

log( number )

Returns the logarithm of the specified number.

Table 62. Parameters
Parameter	Type
`n`	`number`

Example

decimal( log( 10 ), 2 ) = 2.30

exp( number )

Returns Euler’s number e raised to the power of the specified number.

Table 63. Parameters
Parameter	Type
`n`	`number`

Example

decimal( exp( 5 ), 2 ) = 148.41

odd( number )

Returns true if the specified number is odd.

Table 64. Parameters
Parameter	Type
`n`	`number`

Examples

odd( 5 ) = true
odd( 2 ) = false

even( number )

Returns true if the specified number is even.

Table 65. Parameters
Parameter	Type
`n`	`number`

Examples

even( 5 ) = false
even ( 2 ) = true

Date and time functions

The following functions support date and time operations.

is( value1, value2 )

Returns true if both values are the same element in the FEEL semantic domain.

Table 66. Parameters
Parameter	Type
`value1`	Any type
`value2`	Any type

Examples

is( date( "2012-12-25" ), time( "23:00:50" ) ) = false
is( date( "2012-12-25" ), date( "2012-12-25" ) ) = true
is( time( "23:00:50z" ), time( "23:00:50" ) ) = false

Range functions

The following functions support temporal ordering operations to establish relationships between single scalar values and ranges of such values. These functions are similar to the components in the Health Level Seven (HL7) International Clinical Quality Language (CQL) 1.4 syntax.

before( )

Returns true when an element A is before an element B and when the relevant requirements for evaluating to true are also met.

Signatures

before( point1 point2 )
before( point range )
before( range point )
before( range1,range2 )

Requirements for evaluating to true

point1 < point2
point < range.start or ( point = range.start and not(range.start included) )
range.end < point or ( range.end = point and not(range.end included) )
range1.end < range2.start or (( not(range1.end included) or not(range2.start included) ) and range1.end = range2.start )

Examples

before( 1, 10 ) = true
before( 10, 1 ) = false
before( 1, [1..10] ) = false
before( 1, (1..10] ) = true
before( 1, [5..10] ) = true
before( [1..10], 10 ) = false
before( [1..10), 10 ) = true
before( [1..10], 15 ) = true
before( [1..10], [15..20] ) = true
before( [1..10], [10..20] ) = false
before( [1..10), [10..20] ) = true
before( [1..10], (10..20] ) = true

after( )

Returns true when an element A is after an element B and when the relevant requirements for evaluating to true are also met.

Signatures

after( point1 point2 )
after( point range )
after( range, point )
after( range1 range2 )

Requirements for evaluating to true

point1 > point2
point > range.end or ( point = range.end and not(range.end included) )
range.start > point or ( range.start = point and not(range.start included) )
range1.start > range2.end or (( not(range1.start included) or not(range2.end included) ) and range1.start = range2.end )

Examples

after( 10, 5 ) = true
after( 5, 10 ) = false
after( 12, [1..10] ) = true
after( 10, [1..10) ) = true
after( 10, [1..10] ) = false
after( [11..20], 12 ) = false
after( [11..20], 10 ) = true
after( (11..20], 11 ) = true
after( [11..20], 11 ) = false
after( [11..20], [1..10] ) = true
after( [1..10], [11..20] ) = false
after( [11..20], [1..11) ) = true
after( (11..20], [1..11] ) = true

meets( )

Returns true when an element A meets an element B and when the relevant requirements for evaluating to true are also met.

Signatures

meets( range1, range2 )

Requirements for evaluating to true

range1.end included and range2.start included and range1.end = range2.start

Examples

meets( [1..5], [5..10] ) = true
meets( [1..5), [5..10] ) = false
meets( [1..5], (5..10] ) = false
meets( [1..5], [6..10] ) = false

met by( )

Returns true when an element A is met by an element B and when the relevant requirements for evaluating to true are also met.

Signatures

met by( range1, range2 )

Requirements for evaluating to true

range1.start included and range2.end included and range1.start = range2.end

Examples

met by( [5..10], [1..5] ) = true
met by( [5..10], [1..5) ) = false
met by( (5..10], [1..5] ) = false
met by( [6..10], [1..5] ) = false

overlaps( )

Returns true when an element A overlaps an element B and when the relevant requirements for evaluating to true are also met.

Signatures

overlaps( range1, range2 )

Requirements for evaluating to true

( range1.end > range2.start or (range1.end = range2.start and (range1.end included or range2.end included)) ) and ( range1.start < range2.end or (range1.start = range2.end and range1.start included and range2.end included) )

Examples

overlaps( [1..5], [3..8] ) = true
overlaps( [3..8], [1..5] ) = true
overlaps( [1..8], [3..5] ) = true
overlaps( [3..5], [1..8] ) = true
overlaps( [1..5], [6..8] ) = false
overlaps( [6..8], [1..5] ) = false
overlaps( [1..5], [5..8] ) = true
overlaps( [1..5], (5..8] ) = false
overlaps( [1..5), [5..8] ) = false
overlaps( [1..5), (5..8] ) = false
overlaps( [5..8], [1..5] ) = true
overlaps( (5..8], [1..5] ) = false
overlaps( [5..8], [1..5) ) = false
overlaps( (5..8], [1..5) ) = false

overlaps before( )

Returns true when an element A overlaps before an element B and when the relevant requirements for evaluating to true are also met.

Signatures

overlaps before( range1 range2 )

Requirements for evaluating to true

( range1.start < range2.start or (range1.start = range2.start and range1.start included and range2.start included) ) and ( range1.end > range2.start or (range1.end = range2.start and range1.end included and range2.start included) ) and ( range1.end < range2.end or (range1.end = range2.end and (not(range1.end included) or range2.end included )) )

Examples

overlaps before( [1..5], [3..8] ) = true
overlaps before( [1..5], [6..8] ) = false
overlaps before( [1..5], [5..8] ) = true
overlaps before( [1..5], (5..8] ) = false
overlaps before( [1..5), [5..8] ) = false
overlaps before( [1..5), (1..5] ) = true
overlaps before( [1..5], (1..5] ) = true
overlaps before( [1..5), [1..5] ) = false
overlaps before( [1..5], [1..5] ) = false

overlaps after( )

Returns true when an element A overlaps after an element B and when the relevant requirements for evaluating to true are also met.

Signatures

overlaps after( range1 range2 )

Requirements for evaluating to true

( range2.start < range1.start or (range2.start = range1.start and range2.start included and not( range1.start included)) ) and ( range2.end > range1.start or (range2.end = range1.start and range2.end included and range1.start included) ) and ( range2.end < range1.end or (range2.end = range1.end and (not(range2.end included) or range1.end included)) )

Examples

overlaps after( [3..8], [1..5] )= true
overlaps after( [6..8], [1..5] )= false
overlaps after( [5..8], [1..5] )= true
overlaps after( (5..8], [1..5] )= false
overlaps after( [5..8], [1..5) )= false
overlaps after( (1..5], [1..5) )= true
overlaps after( (1..5], [1..5] )= true
overlaps after( [1..5], [1..5) )= false
overlaps after( [1..5], [1..5] )= false
overlaps after( (1..5), [1..5] )= false
overlaps after( (1..5], [1..6] )= false
overlaps after( (1..5], (1..5] )= false
overlaps after( (1..5], [2..5] )= false

finishes( )

Returns true when an element A finishes an element B and when the relevant requirements for evaluating to true are also met.

Signatures

finishes( point, range )
finishes( range1, range2 )

Requirements for evaluating to true

range.end included and range.end = point
range1.end included = range2.end included and range1.end = range2.end and ( range1.start > range2.start or (range1.start = range2.start and (not(range1.start included) or range2.start included)) )

Examples

finishes( 10, [1..10] ) = true
finishes( 10, [1..10) ) = false
finishes( [5..10], [1..10] ) = true
finishes( [5..10), [1..10] ) = false
finishes( [5..10), [1..10) ) = true
finishes( [1..10], [1..10] ) = true
finishes( (1..10], [1..10] ) = true

finished by( )

Returns true when an element A is finished by an element B and when the relevant requirements for evaluating to true are also met.

Signatures

finished by( range, point )
finished by( range1 range2 )

Requirements for evaluating to true

range.end included and range.end = point
range1.end included = range2.end included and range1.end = range2.end and ( range1.start < range2.start or (range1.start = range2.start and (range1.start included or not(range2.start included))) )

Examples

finished by( [1..10], 10 ) = true
finished by( [1..10), 10 ) = false
finished by( [1..10], [5..10] ) = true
finished by( [1..10], [5..10) ) = false
finished by( [1..10), [5..10) ) = true
finished by( [1..10], [1..10] ) = true
finished by( [1..10], (1..10] ) = true

includes( )

Returns true when an element A includes an element B and when the relevant requirements for evaluating to true are also met.

Signatures

includes( range, point )
includes( range1, range2 )

Requirements for evaluating to true

(range.start < point and range.end > point) or (range.start = point and range.start included) or (range.end = point and range.end included)
( range1.start < range2.start or (range1.start = range2.start and (range1.start included or not(range2.start included))) ) and ( range1.end > range2.end or (range1.end = range2.end and (range1.end included or not(range2.end included))) )

Examples

includes( [1..10], 5 ) = true
includes( [1..10], 12 ) = false
includes( [1..10], 1 ) = true
includes( [1..10], 10 ) = true
includes( (1..10], 1 ) = false
includes( [1..10), 10 ) = false
includes( [1..10], [4..6] ) = true
includes( [1..10], [1..5] ) = true
includes( (1..10], (1..5] ) = true
includes( [1..10], (1..10) ) = true
includes( [1..10), [5..10) ) = true
includes( [1..10], [1..10) ) = true
includes( [1..10], (1..10] ) = true
includes( [1..10], [1..10] ) = true

during( )

Returns true when an element A is during an element B and when the relevant requirements for evaluating to true are also met.

Signatures

during( point, range )
during( range1 range2 )

Requirements for evaluating to true

(range.start < point and range.end > point) or (range.start = point and range.start included) or (range.end = point and range.end included)
( range2.start < range1.start or (range2.start = range1.start and (range2.start included or not(range1.start included))) ) and ( range2.end > range1.end or (range2.end = range1.end and (range2.end included or not(range1.end included))) )

Examples

during( 5, [1..10] ) = true
during( 12, [1..10] ) = false
during( 1, [1..10] ) = true
during( 10, [1..10] ) = true
during( 1, (1..10] ) = false
during( 10, [1..10) ) = false
during( [4..6], [1..10] ) = true
during( [1..5], [1..10] ) = true
during( (1..5], (1..10] ) = true
during( (1..10), [1..10] ) = true
during( [5..10), [1..10) ) = true
during( [1..10), [1..10] ) = true
during( (1..10], [1..10] ) = true
during( [1..10], [1..10] ) = true

starts( )

Returns true when an element A starts an element B and when the relevant requirements for evaluating to true are also met.

Signatures

starts( point, range )
starts( range1, range2 )

Requirements for evaluating to true

range.start = point and range.start included
range1.start = range2.start and range1.start included = range2.start included and ( range1.end < range2.end or (range1.end = range2.end and (not(range1.end included) or range2.end included)) )

Examples

starts( 1, [1..10] ) = true
starts( 1, (1..10] ) = false
starts( 2, [1..10] ) = false
starts( [1..5], [1..10] ) = true
starts( (1..5], (1..10] ) = true
starts( (1..5], [1..10] ) = false
starts( [1..5], (1..10] ) = false
starts( [1..10], [1..10] ) = true
starts( [1..10), [1..10] ) = true
starts( (1..10), (1..10) ) = true

started by( )

Returns true when an element A is started by an element B and when the relevant requirements for evaluating to true are also met.

Signatures

started by( range, point )
started by( range1, range2 )

Requirements for evaluating to true

range.start = point and range.start included
range1.start = range2.start and range1.start included = range2.start included and ( range2.end < range1.end or (range2.end = range1.end and (not(range2.end included) or range1.end included)) )

Examples

started by( [1..10], 1 ) = true
started by( (1..10], 1 ) = false
started by( [1..10], 2 ) = false
started by( [1..10], [1..5] ) = true
started by( (1..10], (1..5] ) = true
started by( [1..10], (1..5] ) = false
started by( (1..10], [1..5] ) = false
started by( [1..10], [1..10] ) = true
started by( [1..10], [1..10) ) = true
started by( (1..10), (1..10) ) = true

coincides( )

Returns true when an element A coincides with an element B and when the relevant requirements for evaluating to true are also met.

Signatures

coincides( point1, point2 )
coincides( range1, range2 )

Requirements for evaluating to true

point1 = point2
range1.start = range2.start and range1.start included = range2.start included and range1.end = range2.end and range1.end included = range2.end included

Examples

coincides( 5, 5 ) = true
coincides( 3, 4 ) = false
coincides( [1..5], [1..5] ) = true
coincides( (1..5), [1..5] ) = false
coincides( [1..5], [2..6] ) = false

Temporal functions

The following functions support general temporal operations.

day of year( date )

Returns the Gregorian number of the day of the year.

Table 67. Parameters
Parameter	Type
`date`	`date` or `date and time`

Example

day of year( date(2019, 9, 17) ) = 260

day of week( date )

Returns the Gregorian day of the week: "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", or "Sunday".

Table 68. Parameters
Parameter	Type
`date`	`date` or `date and time`

Example

day of week( date(2019, 9, 17) ) = "Tuesday"

month of year( date )

Returns the Gregorian month of the year: "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", or "December".

Table 69. Parameters
Parameter	Type
`date`	`date` or `date and time`

Example

month of year( date(2019, 9, 17) ) = "September"

month of year( date )

Returns the Gregorian week of the year as defined by ISO 8601.

Table 70. Parameters
Parameter	Type
`date`	`date` or `date and time`

Examples

week of year( date(2019, 9, 17) ) = 38
week of year( date(2003, 12, 29) ) = 1
week of year( date(2004, 1, 4) ) = 1
week of year( date(2005, 1, 1) ) = 53
week of year( date(2005, 1, 3) ) = 1
week of year( date(2005, 1, 9) ) = 1

Sort functions

The following functions support sorting operations.

sort( list, precedes )

Returns a list of the same elements but ordered according to the sorting function.

Table 71. Parameters
Parameter	Type
`list`	`list`
`precedes`	`function`

Example

sort( list: [3,1,4,5,2], precedes: function(x,y) x < y ) = [1,2,3,4,5]

Context functions

The following functions support context operations.

get value( m, key )

Returns the value from the context for the specified entry key.

Table 72. Parameters
Parameter	Type
`m`	`context`
`key`	`string`

Examples

get value( {key1 : "value1"}, "key1" ) = "value1"
get value( {key1 : "value1"}, "unexistent-key" ) = null

get entries( m )

Returns a list of key-value pairs for the specified context.

Table 73. Parameters
Parameter	Type
`m`	`context`

Example

get entries( {key1 : "value1", key2 : "value2"} ) = [ { key : "key1", value : "value1" }, {key : "key2", value : "value2"} ]

Variable and function names in FEEL

Unlike many traditional expression languages, Friendly Enough Expression Language (FEEL) supports spaces and a few special characters as part of variable and function names. A FEEL name must start with a letter, ?, or _ element. The unicode letter characters are also allowed. Variable names cannot start with a language keyword, such as and, true, or every. The remaining characters in a variable name can be any of the starting characters, as well as digits, white spaces, and special characters such as +, -, /, *, ', and ..

For example, the following names are all valid FEEL names:

Age
Birth Date
Flight 234 pre-check procedure

Several limitations apply to variable and function names in FEEL:

Ambiguity: The use of spaces, keywords, and other special characters as part of names can make FEEL ambiguous. The ambiguities are resolved in the context of the expression, matching names from left to right. The parser resolves the variable name as the longest name matched in scope. You can use ( ) to disambiguate names if necessary.
Spaces in names: The DMN specification limits the use of spaces in FEEL names. According to the DMN specification, names can contain multiple spaces but not two consecutive spaces.

In order to make the language easier to use and avoid common errors due to spaces, IBM Business Automation Manager Open Editions removes the limitation on the use of consecutive spaces. IBM Business Automation Manager Open Editions supports variable names with any number of consecutive spaces, but normalizes them into a single space. For example, the variable references First Name with one space and First Name with two spaces are both acceptable in IBM Business Automation Manager Open Editions.

IBM Business Automation Manager Open Editions also normalizes the use of other white spaces, like the non-breakable white space that is common in web pages, tabs, and line breaks. From an IBM Business Automation Manager Open Editions FEEL engine perspective, all of these characters are normalized into a single white space before processing.
The keyword in: The keyword in is the only keyword in the language that cannot be used as part of a variable name. Although the specifications allow the use of keywords in the middle of variable names, the use of in in variable names conflicts with the grammar definition of for, every and some expression constructs.

DMN decision logic in boxed expressions

Boxed expressions in DMN are tables that you use to define the underlying logic of decision nodes and business knowledge models in a decision requirements diagram (DRD). Some boxed expressions can contain other boxed expressions, but the top-level boxed expression corresponds to the decision logic of a single DRD artifact. While DRDs represent the flow of a DMN decision model, boxed expressions define the actual decision logic of individual nodes. DRDs and boxed expressions together form a complete and functional DMN decision model.

The following are the types of DMN boxed expressions:

Decision tables
Literal expressions
Contexts
Relations
Functions
Invocations
Lists

Note

IBM Business Automation Manager Open Editions does not provide boxed list expressions in Business Central, but supports a FEEL list data type that you can use in boxed literal expressions. For more information about the list data type and other FEEL data types in IBM Business Automation Manager Open Editions, see Data types in FEEL.

All Friendly Enough Expression Language (FEEL) expressions that you use in your boxed expressions must conform to the FEEL syntax requirements in the OMG Decision Model and Notation specification.

DMN decision tables

A decision table in DMN is a visual representation of one or more business rules in a tabular format. You use decision tables to define rules for a decision node that applies those rules at a given point in the decision model. 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. The definition of each row is precise enough to derive the outcome using the values of the conditions. Input and output values can be FEEL expressions or defined data type values.

For example, the following decision table determines credit score ratings based on a defined range of a loan applicant’s credit score:

Figure 3. Decision table for credit score rating

The following decision table determines the next step in a lending strategy for applicants depending on applicant loan eligibility and the bureau call type:

Figure 4. Decision table for lending strategy

The following decision table determines applicant qualification for a loan as the concluding decision node in a loan prequalification decision model:

Figure 5. Decision table for loan prequalification

Decision tables are a popular way of modeling rules and decision logic, and are used in many methodologies (such as DMN) and implementation frameworks (such as Drools).

Important

IBM Business Automation Manager Open Editions supports both DMN decision tables and Drools-native decision tables, but they are different types of assets with different syntax requirements and are not interchangeable. For more information about Drools-native decision tables in IBM Business Automation Manager Open Editions, see Designing a decision service using spreadsheet decision tables.

Hit policies in DMN decision tables

Hit policies determine how to reach an outcome when multiple rules in a decision table match the provided input values. For example, if one rule in a decision table applies a sales discount to military personnel and another rule applies a discount to students, then when a customer is both a student and in the military, the decision table hit policy must indicate whether to apply one discount or the other (Unique, First) or both discounts (Collect Sum). You specify the single character of the hit policy (U, F, C+) in the upper-left corner of the decision table.

The following decision table hit policies are supported in DMN:

Unique (U): Permits only one rule to match. Any overlap raises an error.
Any (A): Permits multiple rules to match, but they must all have the same output. If multiple matching rules do not have the same output, an error is raised.
Priority (P): Permits multiple rules to match, with different outputs. The output that comes first in the output values list is selected.
First (F): Uses the first match in rule order.
Collect (C+, C>, C<, C#): Aggregates output from multiple rules based on an aggregation function.
- Collect ( C ): Aggregates values in an arbitrary list.
- Collect Sum (C+): Outputs the sum of all collected values. Values must be numeric.
- Collect Min (C<): Outputs the minimum value among the matches. The resulting values must be comparable, such as numbers, dates, or text (lexicographic order).
- Collect Max (C>): Outputs the maximum value among the matches. The resulting values must be comparable, such as numbers, dates or text (lexicographic order).
- Collect Count (C#): Outputs the number of matching rules.

Boxed literal expressions

A boxed literal expression in DMN is a literal FEEL expression as text in a table cell, typically with a labeled column and an assigned data type. You use boxed literal expressions to define simple or complex node logic or decision data directly in FEEL for a particular node in a decision. Literal FEEL expressions must conform to FEEL syntax requirements in the OMG Decision Model and Notation specification.

For example, the following boxed literal expression defines the minimum acceptable PITI calculation (principal, interest, taxes, and insurance) in a lending decision, where acceptable rate is a variable defined in the DMN model:

Figure 6. Boxed literal expression for minimum PITI value

The following boxed literal expression sorts a list of possible dating candidates (soul mates) in an online dating application based on their score on criteria such as age, location, and interests:

Figure 7. Boxed literal expression for matching online dating candidates

Boxed context expressions

A boxed context expression in DMN is a set of variable names and values with a result value. Each name-value pair is a context entry. You use context expressions to represent data definitions in decision logic and set a value for a desired decision element within the DMN decision model. A value in a boxed context expression can be a data type value or FEEL expression, or can contain a nested sub-expression of any type, such as a decision table, a literal expression, or another context expression.

For example, the following boxed context expression defines the factors for sorting delayed passengers in a flight-rebooking decision model, based on defined data types (tPassengerTable, tFlightNumberList):

Figure 8. Boxed context expression for flight passenger waiting list

The following boxed context expression defines the factors that determine whether a loan applicant can meet minimum mortgage payments based on principal, interest, taxes, and insurance (PITI), represented as a front-end ratio calculation with a sub-context expression:

Figure 9. Boxed context expression for front-end client PITI ratio

Boxed relation expressions

A boxed relation expression in DMN is a traditional data table with information about given entities, listed as rows. You use boxed relation tables to define decision data for relevant entities in a decision at a particular node. Boxed relation expressions are similar to context expressions in that they set variable names and values, but relation expressions contain no result value and list all variable values based on a single defined variable in each column.

For example, the following boxed relation expression provides information about employees in an employee rostering decision:

Figure 10. Boxed relation expression with employee information

Boxed function expressions

A boxed function expression in DMN is a parameterized boxed expression containing a literal FEEL expression, a nested context expression of an external JAVA or PMML function, or a nested boxed expression of any type. By default, all business knowledge models are defined as boxed function expressions. You use boxed function expressions to call functions on your decision logic and to define all business knowledge models.

For example, the following boxed function expression determines airline flight capacity in a flight-rebooking decision model:

Figure 11. Boxed function expression for flight capacity

The following boxed function expression contains a basic Java function as a context expression for determining absolute value in a decision model calculation:

Figure 12. Boxed function expression for absolute value

The following boxed function expression determines a monthly mortgage installment as a business knowledge model in a lending decision, with the function value defined as a nested context expression:

Figure 13. Boxed function expression for installment calculation in business knowledge model

The following boxed function expression uses a PMML model included in the DMN file to define the minimum acceptable PITI calculation (principal, interest, taxes, and insurance) in a lending decision:

Figure 14. Boxed function expression with an included PMML model in business knowledge model

Boxed invocation expressions

A boxed invocation expression in DMN is a boxed expression that invokes a business knowledge model. A boxed invocation expression contains the name of the business knowledge model to be invoked and a list of parameter bindings. Each binding is represented by two boxed expressions on a row: The box on the left contains the name of a parameter and the box on the right contains the binding expression whose value is assigned to the parameter to evaluate the invoked business knowledge model. You use boxed invocations to invoke at a particular decision node a business knowledge model defined in the decision model.

For example, the following boxed invocation expression invokes a Reassign Next Passenger business knowledge model as the concluding decision node in a flight-rebooking decision model:

Figure 15. Boxed invocation expression to reassign flight passengers

The following boxed invocation expression invokes an InstallmentCalculation business knowledge model to calculate a monthly installment amount for a loan before proceeding to affordability decisions:

Figure 16. Boxed invocation expression for required monthly installment

Boxed list expressions

A boxed list expression in DMN represents a FEEL list of items. You use boxed lists to define lists of relevant items for a particular node in a decision. You can also use literal FEEL expressions for list items in cells to create more complex lists.

For example, the following boxed list expression identifies approved credit score agencies in a loan application decision service:

Figure 17. Boxed list expression for approved credit score agencies

The following boxed list expression also identifies approved credit score agencies but uses FEEL logic to define the agency status (Inc., LLC, SA, GA) based on a DMN input node:

Figure 18. Boxed list expression using FEEL logic for approved credit score agency status

DMN model example

The following is a real-world DMN model example that demonstrates how you can use decision modeling to reach a decision based on input data, circumstances, and company guidelines. In this scenario, a flight from San Diego to New York is canceled, requiring the affected airline to find alternate arrangements for its inconvenienced passengers.

First, the airline collects the information necessary to determine how best to get the travelers to their destinations:

Input data

List of flights
List of passengers

Decisions

Prioritize the passengers who will get seats on a new flight
Determine which flights those passengers will be offered

Business knowledge models

The company process for determining passenger priority
Any flights that have space available
Company rules for determining how best to reassign inconvenienced passengers

The airline then uses the DMN standard to model its decision process in the following decision requirements diagram (DRD) for determining the best rebooking solution:

Figure 19. DRD for flight rebooking

Similar to flowcharts, DRDs use shapes to represent the different elements in a process. Ovals contain the two necessary input data, rectangles contain the decision points in the model, and rectangles with clipped corners (business knowledge models) contain reusable logic that can be repeatedly invoked.

The DRD draws logic for each element from boxed expressions that provide variable definitions using FEEL expressions or data type values.

Some boxed expressions are basic, such as the following decision for establishing a prioritized waiting list:

Figure 20. Boxed context expression example for prioritized wait list

Some boxed expressions are more complex with greater detail and calculation, such as the following business knowledge model for reassigning the next delayed passenger:

Figure 21. Boxed function expression for passenger reassignment

The following is the DMN source file for this decision model:

<dmn:definitions xmlns="https://www.drools.org/kie-dmn/Flight-rebooking" xmlns:dmn="http://www.omg.org/spec/DMN/20151101/dmn.xsd" xmlns:feel="http://www.omg.org/spec/FEEL/20140401" id="_0019_flight_rebooking" name="0019-flight-rebooking" namespace="https://www.drools.org/kie-dmn/Flight-rebooking">
  <dmn:itemDefinition id="_tFlight" name="tFlight">
    <dmn:itemComponent id="_tFlight_Flight" name="Flight Number">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_From" name="From">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_To" name="To">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Dep" name="Departure">
      <dmn:typeRef>feel:dateTime</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Arr" name="Arrival">
      <dmn:typeRef>feel:dateTime</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Capacity" name="Capacity">
      <dmn:typeRef>feel:number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tFlight_Status" name="Status">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tFlightTable" isCollection="true" name="tFlightTable">
    <dmn:typeRef>tFlight</dmn:typeRef>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tPassenger" name="tPassenger">
    <dmn:itemComponent id="_tPassenger_Name" name="Name">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tPassenger_Status" name="Status">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tPassenger_Miles" name="Miles">
      <dmn:typeRef>feel:number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_tPassenger_Flight" name="Flight Number">
      <dmn:typeRef>feel:string</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tPassengerTable" isCollection="true" name="tPassengerTable">
    <dmn:typeRef>tPassenger</dmn:typeRef>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_tFlightNumberList" isCollection="true" name="tFlightNumberList">
    <dmn:typeRef>feel:string</dmn:typeRef>
  </dmn:itemDefinition>
  <dmn:inputData id="i_Flight_List" name="Flight List">
    <dmn:variable name="Flight List" typeRef="tFlightTable"/>
  </dmn:inputData>
  <dmn:inputData id="i_Passenger_List" name="Passenger List">
    <dmn:variable name="Passenger List" typeRef="tPassengerTable"/>
  </dmn:inputData>
  <dmn:decision name="Prioritized Waiting List" id="d_PrioritizedWaitingList">
    <dmn:variable name="Prioritized Waiting List" typeRef="tPassengerTable"/>
    <dmn:informationRequirement>
      <dmn:requiredInput href="#i_Passenger_List"/>
    </dmn:informationRequirement>
    <dmn:informationRequirement>
      <dmn:requiredInput href="#i_Flight_List"/>
    </dmn:informationRequirement>
    <dmn:knowledgeRequirement>
      <dmn:requiredKnowledge href="#b_PassengerPriority"/>
    </dmn:knowledgeRequirement>
    <dmn:context>
      <dmn:contextEntry>
        <dmn:variable name="Cancelled Flights" typeRef="tFlightNumberList"/>
        <dmn:literalExpression>
          <dmn:text>Flight List[ Status = "cancelled" ].Flight Number</dmn:text>
        </dmn:literalExpression>
      </dmn:contextEntry>
      <dmn:contextEntry>
        <dmn:variable name="Waiting List" typeRef="tPassengerTable"/>
        <dmn:literalExpression>
          <dmn:text>Passenger List[ list contains( Cancelled Flights, Flight Number ) ]</dmn:text>
        </dmn:literalExpression>
      </dmn:contextEntry>
      <dmn:contextEntry>
        <dmn:literalExpression>
          <dmn:text>sort( Waiting List, passenger priority )</dmn:text>
        </dmn:literalExpression>
      </dmn:contextEntry>
    </dmn:context>
  </dmn:decision>
  <dmn:decision name="Rebooked Passengers" id="d_RebookedPassengers">
    <dmn:variable name="Rebooked Passengers" typeRef="tPassengerTable"/>
    <dmn:informationRequirement>
      <dmn:requiredDecision href="#d_PrioritizedWaitingList"/>
    </dmn:informationRequirement>
    <dmn:informationRequirement>
      <dmn:requiredInput href="#i_Flight_List"/>
    </dmn:informationRequirement>
    <dmn:knowledgeRequirement>
      <dmn:requiredKnowledge href="#b_ReassignNextPassenger"/>
    </dmn:knowledgeRequirement>
    <dmn:invocation>
      <dmn:literalExpression>
        <dmn:text>reassign next passenger</dmn:text>
      </dmn:literalExpression>
      <dmn:binding>
        <dmn:parameter name="Waiting List"/>
        <dmn:literalExpression>
          <dmn:text>Prioritized Waiting List</dmn:text>
        </dmn:literalExpression>
      </dmn:binding>
      <dmn:binding>
        <dmn:parameter name="Reassigned Passengers List"/>
        <dmn:literalExpression>
          <dmn:text>[]</dmn:text>
        </dmn:literalExpression>
      </dmn:binding>
      <dmn:binding>
        <dmn:parameter name="Flights"/>
        <dmn:literalExpression>
          <dmn:text>Flight List</dmn:text>
        </dmn:literalExpression>
      </dmn:binding>
    </dmn:invocation>
  </dmn:decision>
  <dmn:businessKnowledgeModel id="b_PassengerPriority" name="passenger priority">
    <dmn:encapsulatedLogic>
      <dmn:formalParameter name="Passenger1" typeRef="tPassenger"/>
      <dmn:formalParameter name="Passenger2" typeRef="tPassenger"/>
      <dmn:decisionTable hitPolicy="UNIQUE">
        <dmn:input id="b_Passenger_Priority_dt_i_P1_Status" label="Passenger1.Status">
          <dmn:inputExpression typeRef="feel:string">
            <dmn:text>Passenger1.Status</dmn:text>
          </dmn:inputExpression>
          <dmn:inputValues>
            <dmn:text>"gold", "silver", "bronze"</dmn:text>
          </dmn:inputValues>
        </dmn:input>
        <dmn:input id="b_Passenger_Priority_dt_i_P2_Status" label="Passenger2.Status">
          <dmn:inputExpression typeRef="feel:string">
            <dmn:text>Passenger2.Status</dmn:text>
          </dmn:inputExpression>
          <dmn:inputValues>
            <dmn:text>"gold", "silver", "bronze"</dmn:text>
          </dmn:inputValues>
        </dmn:input>
        <dmn:input id="b_Passenger_Priority_dt_i_P1_Miles" label="Passenger1.Miles">
          <dmn:inputExpression typeRef="feel:string">
            <dmn:text>Passenger1.Miles</dmn:text>
          </dmn:inputExpression>
        </dmn:input>
        <dmn:output id="b_Status_Priority_dt_o" label="Passenger1 has priority">
          <dmn:outputValues>
            <dmn:text>true, false</dmn:text>
          </dmn:outputValues>
          <dmn:defaultOutputEntry>
            <dmn:text>false</dmn:text>
          </dmn:defaultOutputEntry>
        </dmn:output>
        <dmn:rule id="b_Passenger_Priority_dt_r1">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r1_i1">
            <dmn:text>"gold"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r1_i2">
            <dmn:text>"gold"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r1_i3">
            <dmn:text>>= Passenger2.Miles</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r1_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r2">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r2_i1">
            <dmn:text>"gold"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r2_i2">
            <dmn:text>"silver","bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r2_i3">
            <dmn:text>-</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r2_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r3">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r3_i1">
            <dmn:text>"silver"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r3_i2">
            <dmn:text>"silver"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r3_i3">
            <dmn:text>>= Passenger2.Miles</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r3_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r4">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r4_i1">
            <dmn:text>"silver"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r4_i2">
            <dmn:text>"bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r4_i3">
            <dmn:text>-</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r4_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
        <dmn:rule id="b_Passenger_Priority_dt_r5">
          <dmn:inputEntry id="b_Passenger_Priority_dt_r5_i1">
            <dmn:text>"bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r5_i2">
            <dmn:text>"bronze"</dmn:text>
          </dmn:inputEntry>
          <dmn:inputEntry id="b_Passenger_Priority_dt_r5_i3">
            <dmn:text>>= Passenger2.Miles</dmn:text>
          </dmn:inputEntry>
          <dmn:outputEntry id="b_Passenger_Priority_dt_r5_o1">
            <dmn:text>true</dmn:text>
          </dmn:outputEntry>
        </dmn:rule>
      </dmn:decisionTable>
    </dmn:encapsulatedLogic>
    <dmn:variable name="passenger priority" typeRef="feel:boolean"/>
  </dmn:businessKnowledgeModel>
  <dmn:businessKnowledgeModel id="b_ReassignNextPassenger" name="reassign next passenger">
    <dmn:encapsulatedLogic>
      <dmn:formalParameter name="Waiting List" typeRef="tPassengerTable"/>
      <dmn:formalParameter name="Reassigned Passengers List" typeRef="tPassengerTable"/>
      <dmn:formalParameter name="Flights" typeRef="tFlightTable"/>
      <dmn:context>
        <dmn:contextEntry>
          <dmn:variable name="Next Passenger" typeRef="tPassenger"/>
          <dmn:literalExpression>
            <dmn:text>Waiting List[1]</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Original Flight" typeRef="tFlight"/>
          <dmn:literalExpression>
            <dmn:text>Flights[ Flight Number = Next Passenger.Flight Number ][1]</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Best Alternate Flight" typeRef="tFlight"/>
          <dmn:literalExpression>
            <dmn:text>Flights[ From = Original Flight.From and To = Original Flight.To and Departure > Original Flight.Departure and Status = "scheduled" and has capacity( item, Reassigned Passengers List ) ][1]</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Reassigned Passenger" typeRef="tPassenger"/>
          <dmn:context>
            <dmn:contextEntry>
              <dmn:variable name="Name" typeRef="feel:string"/>
              <dmn:literalExpression>
                <dmn:text>Next Passenger.Name</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
            <dmn:contextEntry>
              <dmn:variable name="Status" typeRef="feel:string"/>
              <dmn:literalExpression>
                <dmn:text>Next Passenger.Status</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
            <dmn:contextEntry>
              <dmn:variable name="Miles" typeRef="feel:number"/>
              <dmn:literalExpression>
                <dmn:text>Next Passenger.Miles</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
            <dmn:contextEntry>
              <dmn:variable name="Flight Number" typeRef="feel:string"/>
              <dmn:literalExpression>
                <dmn:text>Best Alternate Flight.Flight Number</dmn:text>
              </dmn:literalExpression>
            </dmn:contextEntry>
          </dmn:context>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Remaining Waiting List" typeRef="tPassengerTable"/>
          <dmn:literalExpression>
            <dmn:text>remove( Waiting List, 1 )</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:variable name="Updated Reassigned Passengers List" typeRef="tPassengerTable"/>
          <dmn:literalExpression>
            <dmn:text>append( Reassigned Passengers List, Reassigned Passenger )</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
        <dmn:contextEntry>
          <dmn:literalExpression>
            <dmn:text>if count( Remaining Waiting List ) > 0 then reassign next passenger( Remaining Waiting List, Updated Reassigned Passengers List, Flights ) else Updated Reassigned Passengers List</dmn:text>
          </dmn:literalExpression>
        </dmn:contextEntry>
      </dmn:context>
    </dmn:encapsulatedLogic>
    <dmn:variable name="reassign next passenger" typeRef="tPassengerTable"/>
    <dmn:knowledgeRequirement>
      <dmn:requiredKnowledge href="#b_HasCapacity"/>
    </dmn:knowledgeRequirement>
  </dmn:businessKnowledgeModel>
  <dmn:businessKnowledgeModel id="b_HasCapacity" name="has capacity">
    <dmn:encapsulatedLogic>
      <dmn:formalParameter name="flight" typeRef="tFlight"/>
      <dmn:formalParameter name="rebooked list" typeRef="tPassengerTable"/>
      <dmn:literalExpression>
        <dmn:text>flight.Capacity > count( rebooked list[ Flight Number = flight.Flight Number ] )</dmn:text>
      </dmn:literalExpression>
    </dmn:encapsulatedLogic>
    <dmn:variable name="has capacity" typeRef="feel:boolean"/>
  </dmn:businessKnowledgeModel>
</dmn:definitions>

DMN support in IBM Business Automation Manager Open Editions

Design your DMN models directly in Business Central using the DMN designer.
Import DMN files into your project in Business Central (Menu → Design → Projects → Import Asset). Any DMN 1.1 and 1.3 models (do not contain DMN 1.3 features) that you import into Business Central, open in the DMN designer, and save are converted to DMN 1.2 models.
Package DMN files as part of your project knowledge JAR (KJAR) file without Business Central.

The following table summarizes the design and runtime support for each DMN version in IBM Business Automation Manager Open Editions:

Table 74. DMN support in IBM Business Automation Manager Open Editions
DMN version	DMN engine support	DMN modeler support
DMN version	Execution	Open	Save
DMN 1.1
DMN 1.2
DMN 1.3
DMN 1.4

In addition to all DMN conformance level 3 requirements, IBM Business Automation Manager Open Editions also includes enhancements and fixes to FEEL and DMN model components to optimize the experience of implementing DMN decision services with IBM Business Automation Manager Open Editions. From a platform perspective, DMN models are like any other business asset in IBM Business Automation Manager Open Editions, such as DRL files or spreadsheet decision tables, that you can include in your IBM Business Automation Manager Open Editions project and deploy to KIE Server in order to start your DMN decision services.

For more information about including external DMN files with your IBM Business Automation Manager Open Editions project packaging and deployment method, see Packaging and deploying an IBM Business Automation Manager Open Editions project.

You can design a new DMN decision service using a Red Hat build of Kogito microservice as an alternative for the cloud-native capabilities of DMN decision services. You can migrate your DMN service to a Red Hat build of Kogito microservice. For more information about migrating to Red Hat build of Kogito microservices, see Migrating to Red Hat build of Kogito microservices.

Configurable DMN properties in IBM Business Automation Manager Open Editions

IBM Business Automation Manager Open Editions provides the following DMN properties that you can configure when you execute your DMN models on KIE Server or on your client application. You can configure some of these properties using the kmodule.xml file in your IBM Business Automation Manager Open Editions project when you deploy your project on KIE Server.

org.kie.dmn.strictConformance

When enabled, this property disables by default any extensions or profiles provided beyond the DMN standard, such as some helper functions or enhanced features of DMN 1.2 backported into DMN 1.1. You can use this property to configure the decision engine to support only pure DMN features, such as when running the DMN Technology Compatibility Kit (TCK).

Default value: false

-Dorg.kie.dmn.strictConformance=true

org.kie.dmn.runtime.typecheck

When enabled, this property enables verification of actual values conforming to their declared types in the DMN model, as input or output of DRD elements. You can use this property to verify whether data supplied to the DMN model or produced by the DMN model is compliant with what is specified in the model.

Default value: false

-Dorg.kie.dmn.runtime.typecheck=true

org.kie.dmn.decisionservice.coercesingleton

By default, this property makes the result of a decision service defining a single output decision be the single value of the output decision value. When disabled, this property makes the result of a decision service defining a single output decision be a context with the single entry for that decision. You can use this property to adjust your decision service outputs according to your project requirements.

Default value: true

-Dorg.kie.dmn.decisionservice.coercesingleton=false

org.kie.dmn.profiles.$PROFILE_NAME

When valorized with a Java fully qualified name, this property loads a DMN profile onto the decision engine at start time. You can use this property to implement a predefined DMN profile with supported features different from or beyond the DMN standard. For example, if you are creating DMN models using the Signavio DMN modeller, use this property to implement features from the Signavio DMN profile into your DMN decision service.

-Dorg.kie.dmn.profiles.signavio=org.kie.dmn.signavio.KieDMNSignavioProfile

org.kie.dmn.runtime.listeners.$LISTENER_NAME

When valorized with a Java fully qualified name, this property loads and registers a DMN Runtime Listener onto the decision engine at start time. You can use this property to register a DMN listener in order to be notified of several events during DMN model evaluations.

To configure this property when deploying your project on KIE Server, modify this property in the kmodule.xml file of your project. This approach is helpful when the listener is specific to your project and when the configuration must be applied in KIE Server only to your deployed project.

<kmodule xmlns="http://www.drools.org/xsd/kmodule">
  <configuration>
    <property key="org.kie.dmn.runtime.listeners.mylistener" value="org.acme.MyDMNListener"/>
  </configuration>
</kmodule>

To configure this property globally for your IBM Business Automation Manager Open Editions environment, modify this property using a command terminal or any other global application configuration mechanism. This approach is helpful when the decision engine is embedded as part of your Java application.

-Dorg.kie.dmn.runtime.listeners.mylistener=org.acme.MyDMNListener

org.kie.dmn.compiler.execmodel

When enabled, this property enables DMN decision table logic to be compiled into executable rule models during run time. You can use this property to evaluate DMN decision table logic more efficiently. This property is helpful when the executable model compilation was not originally performed during project compile time. Enabling this property may result in added compile time during the first evaluation by the decision engine, but subsequent compilations are more efficient.

Default value: false

-Dorg.kie.dmn.compiler.execmodel=true

Configurable DMN validation in IBM Business Automation Manager Open Editions

By default, the kie-maven-plugin component in the pom.xml file of your IBM Business Automation Manager Open Editions project uses the following <validateDMN> configurations to perform pre-compilation validation of DMN model assets and to perform DMN decision table static analysis:

VALIDATE_SCHEMA: DMN model files are verified against the DMN specification XSD schema to ensure that the files are valid XML and compliant with the specification.
VALIDATE_MODEL: The pre-compilation analysis is performed for the DMN model to ensure that the basic semantic is aligned with the DMN specification.
ANALYZE_DECISION_TABLE: DMN decision tables are statically analyzed for gaps or overlaps and to ensure that the semantic of the decision table follows best practices.

You can modify the default DMN validation and DMN decision table analysis behavior to perform only a specified validation during the project build, or you can disable this default behavior completely, as shown in the following examples:

Default configuration for DMN validation and decision table analysis

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>VALIDATE_SCHEMA,VALIDATE_MODEL,ANALYZE_DECISION_TABLE</validateDMN>
  </configuration>
</plugin>

Configuration to perform only the DMN decision table static analysis

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>ANALYZE_DECISION_TABLE</validateDMN>
  </configuration>
</plugin>

Configuration to perform only the XSD schema validation

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>VALIDATE_SCHEMA</validateDMN>
  </configuration>
</plugin>

Configuration to perform only the DMN model validation

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>VALIDATE_MODEL</validateDMN>
  </configuration>
</plugin>

Configuration to disable all DMN validation

<plugin>
  <groupId>org.kie</groupId>
  <artifactId>kie-maven-plugin</artifactId>
  <extensions>true</extensions>
  <configuration>
    <validateDMN>disable</validateDMN>
  </configuration>
</plugin>

Note	If you enter an unrecognized `<validateDMN>` configuration flag, all pre-compilation validation is disabled and the Maven plugin emits related log messages.

Creating and editing DMN models in Business Central

You can use the DMN designer in Business Central to design DMN decision requirements diagrams (DRDs) and define decision logic for a complete and functional DMN decision model. IBM Business Automation Manager Open Editions provides design support for DMN 1.2 models at conformance level 3, and includes enhancements and fixes to FEEL and DMN model components to optimize the experience of implementing DMN decision services with IBM Business Automation Manager Open Editions. IBM Business Automation Manager Open Editions also provides runtime support for DMN 1.1, 1.2, 1.3, and 1.4 models at conformance level 3, but any DMN 1.1 and 1.3 models (do not contain DMN 1.3 features) that you import into Business Central, open in the DMN designer, and save are converted to DMN 1.2 models.

Procedure

In Business Central, go to Menu → Design → Projects and click the project name.

Create or import a DMN file in your Business Central project.

To create a DMN file, click Add Asset → DMN, enter an informative DMN model name, select the appropriate Package, and click Ok.

To import an existing DMN file, click Import Asset, enter the DMN model name, select the appropriate Package, select the DMN file to upload, and click Ok.

The new DMN file is now listed in the DMN panel of the Project Explorer, and the DMN decision requirements diagram (DRD) canvas appears.

Note

If you imported a DMN file that does not contain layout information, the imported decision requirements diagram (DRD) is formatted automatically in the DMN designer. Click Save in the DMN designer to save the DRD layout.

If an imported DRD is not automatically formatted, you can select the Perform automatic layout icon in the upper-right toolbar in the DMN designer to format the DRD.

Begin adding components to your new or imported DMN decision requirements diagram (DRD) by clicking and dragging one of the DMN nodes from the left toolbar:
Figure 22. Adding DRD components

The following DRD components are available:
- Decision: Use this node for a DMN decision, where one or more input elements determine an output based on defined decision logic.
- Business knowledge model: Use this node for reusable functions with one or more decision elements. Decisions that have the same logic but depend on different sub-input data or sub-decisions use business knowledge models to determine which procedure to follow.
- Knowledge source: Use this node for external authorities, documents, committees, or policies that regulate a decision or business knowledge model. Knowledge sources are references to real-world factors rather than executable business rules.
- Input data: Use this node for information used in a decision node or a business knowledge model. Input data usually includes business-level concepts or objects relevant to the business, such as loan applicant data used in a lending strategy.
- Text annotation: Use this node for explanatory notes associated with an input data node, decision node, business knowledge model, or knowledge source.
- Decision service: Use this node to enclose a set of reusable decisions implemented as a decision service for invocation. A decision service can be used in other DMN models and can be invoked from an external application or a BPMN business process.
In the DMN designer canvas, double-click the new DRD node to enter an informative node name.
If the node is a decision or business knowledge model, select the node to display the node options and click the Edit icon to open the DMN boxed expression designer to define the decision logic for the node:

Figure 23. Opening a new decision node boxed expression

Figure 24. 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 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.

Figure 25. Selecting the logic type for a decision node

For business knowledge models, you click the top-left function cell to select the function type, or right-click the function value cell, select Clear, and select a boxed expression of another type.

Figure 26. Selecting the function or other logic type for a business knowledge model
In the selected boxed expression designer for either a decision node (any expression type) or business knowledge model (function expression), click the applicable table cells to define the table name, variable data types, variable names and values, function parameters and bindings, or FEEL expressions to include in the decision logic.

You can right-click cells for additional actions where applicable, such as inserting or removing table rows and columns or clearing table contents.

The following is an example decision table for a decision node that determines credit score ratings based on a defined range of a loan applicant’s credit score:

Figure 27. Decision node decision table for credit score rating

The following is an example boxed function expression for a business knowledge model that calculates mortgage payments based on principal, interest, taxes, and insurance (PITI) as a literal expression:

Figure 28. Business knowledge model function for PITI calculation
After you define the decision logic for the selected node, click Back to "<MODEL_NAME>" to return to the DRD view.
For the selected DRD node, use the available connection options to create and connect to the next node in the DRD, or click and drag a new node onto the DRD canvas from the left toolbar.
The node type determines which connection options are supported. For example, an Input data node can connect to a decision node, knowledge source, or text annotation using the applicable connection type, whereas a Knowledge source node can connect to any DRD element. A Decision node can connect only to another decision or a text annotation.

The following connection types are available, depending on the node type:
- Information requirement: Use this connection from an input data node or decision node to another decision node that requires the information.
- Knowledge requirement: Use this connection from a business knowledge model to a decision node or to another business knowledge model that invokes the decision logic.
- Authority requirement: Use this connection from an input data node or a decision node to a dependent knowledge source or from a knowledge source to a decision node, business knowledge model, or another knowledge source.
- Association: Use this connection from an input data node, decision node, business knowledge model, or knowledge source to a text annotation.
Figure 29. Connecting credit score input to the credit score rating decision

Continue adding and defining the remaining DRD components of your decision model. Periodically click Save in the DMN designer to save your work.

Note	As you periodically save a DRD, the DMN designer performs a static validation of the DMN model and might produce error messages until the model is defined completely. After you finish defining the DMN model completely, if any errors remain, troubleshoot the specified problems accordingly.

After you add and define all components of the DRD, click Save to save and validate the completed DRD.

To adjust the DRD layout, you can select the Perform automatic layout icon in the upper-right toolbar of the DMN designer.

The following is an example DRD for a loan prequalification decision model:

Figure 30. Completed DRD for loan prequalification

The following is an example DRD for a phone call handling decision model using a reusable decision service:

Figure 31. Completed DRD for phone call handling with a decision service

In a DMN decision service node, the decision nodes in the bottom segment incorporate input data from outside of the decision service to arrive at a final decision in the top segment of the decision service node. The resulting top-level decisions from the decision service are then implemented in any subsequent decisions or business knowledge requirements of the DMN model. You can reuse DMN decision services in other DMN models to apply the same decision logic with different input data and different outgoing connections.

Defining DMN decision logic in boxed expressions in Business Central

You can use the DMN designer in Business Central to define decision logic for your DRD components using built-in boxed expressions.

Prerequisites

A DMN file is created or imported in Business Central.

Procedure

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

Figure 32. Opening a new decision node boxed expression

Figure 33. 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 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.

Figure 34. Selecting the logic type for a decision node

For business knowledge model nodes, you click the top-left function cell to select the function type, or right-click the function value cell, select Clear, and select a boxed expression of another type.

Figure 35. Selecting the function or other logic type for a business knowledge model
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.
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.
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 go to the Data Types page where you can create a custom data type with score ratings as constraints.

Figure 36. Managing data types for a column header value
On the Data Types page, click New Data Type to add a new data type or click Import Data Object to import an existing data object from your project that you want to use as a DMN data type.

If you import a data object from your project as a DMN data type and then that object is updated, you must re-import the data object as a DMN data type to apply the changes in your DMN model.

For this example, click New Data Type and create a Credit_Score_Rating data type as a string:

Figure 37. Adding a new data type
Click Add Constraints, select Enumeration from the drop-down options, and add the following constraints:
- "Excellent"
- "Good"
- "Fair"
- "Poor"
- "Bad"
Figure 38. Adding constraints to the new data type

To change the order of data type constraints, you can click the left end of the constraint row and drag the row as needed:

Figure 39. Dragging constraints to change constraint order

For information about constraint types and syntax requirements for the specified data type, see the Decision Model and Notation specification.
Click OK to save the constraints and click the check mark to the right of the data type to save the data type.
Return to the Credit Score Rating decision table, click the Credit Score Rating column header, and set the data type to this new custom data type.
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 40. Decision node decision table for credit score rating
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.

Figure 41. Defining the decision table hit policy

The following example 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 42. Decision table for loan prequalification

For boxed expression types other than decision tables, you follow these guidelines similarly to navigate the boxed expression tables and define variables and parameters for decision logic, but according to the requirements of the boxed expression type. Some boxed expressions, such as boxed literal expressions, can be single-column tables, while other boxed expressions, such as function, context, and invocation expressions, can be multi-column tables with nested boxed expressions of other types.

For example, the following boxed context expression defines the parameters that determine whether a loan applicant can meet minimum mortgage payments based on principal, interest, taxes, and insurance (PITI), represented as a front-end ratio calculation with a sub-context expression:

Figure 43. Boxed context expression for front-end client PITI ratio

The following boxed function expression determines a monthly mortgage installment as a business knowledge model in a lending decision, with the function value defined as a nested context expression:

Figure 44. Boxed function expression for installment calculation in business knowledge model

For more information and examples of each boxed expression type, see DMN decision logic in boxed expressions.

Creating custom data types for DMN boxed expressions in Business Central

In DMN boxed expressions in Business Central, data types determine the structure of the data that you use within an associated table, column, or field in the boxed expression. You can use default DMN data types (such as String, Number, Boolean) or you can create custom data types to specify additional fields and constraints that you want to implement for the boxed expression values.

Custom data types that you create for a boxed expression can be simple or structured:

Simple data types have only a name and a type assignment. Example: Age (number).
Structured data types contain multiple fields associated with a parent data type. Example: A single type Person containing the fields Name (string), Age (number), Email (string).

Prerequisites

A DMN file is created or imported in Business Central.

Procedure

In Business Central, go to Menu → Design → Projects, click the project name, and select the DMN file you want to modify.
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.
If the boxed expression is for a decision node that is not yet defined, 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.

Figure 45. Selecting the logic type for a decision node
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 go to the Data Types page where you can create a custom data type.

Figure 46. Managing data types for a column header value

You can also set and manage custom data types for a specified decision node or business knowledge model node by selecting the Properties icon in the upper-right corner of the DMN designer:

Figure 47. Managing data types in decision requirements diagram (DRD) properties

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.
On the Data Types page, click New Data Type to add a new data type or click Import Data Object to import an existing data object from your project that you want to use as a DMN data type.

If you import a data object from your project as a DMN data type and then that object is updated, you must re-import the data object as a DMN data type to apply the changes in your DMN model.

For this example, click New Data Type and create a Credit_Score_Rating data type as a string:

Figure 48. Adding a new data type

If the data type requires a list of items, enable the List setting.
Click Add Constraints, select Enumeration from the drop-down options, and add the following constraints:
- "Excellent"
- "Good"
- "Fair"
- "Poor"
- "Bad"
Figure 49. Adding constraints to the new data type

To change the order of data type constraints, you can click the left end of the constraint row and drag the row as needed:

Figure 50. Dragging constraints to change constraint order

For information about constraint types and syntax requirements for the specified data type, see the Decision Model and Notation specification.
Click OK to save the constraints and click the check mark to the right of the data type to save the data type.
Return to the Credit Score Rating decision table, 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 51. 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:

Figure 52. Decision table for loan prequalification
Continuing with this example, return to the Data Types window, click New Data Type, and create a Loan_Qualification data type as a Structure with no constraints.

When you save the new structured data type, the first sub-field appears so that you can begin defining nested data fields in 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.

For additional sub-fields, select the addition icon next to the Loan_Qualification data type:

Figure 53. Adding a new structured data type with nested fields
For this example, under the structured Loan_Qualification data type, add a Qualification field with "Qualified" and "Not Qualified" enumeration constraints, and a Reason field with no constraints. Add also a simple Back_End_Ratio and a Front_End_Ratio data type, both with "Sufficient" and "Insufficient" enumeration constraints.

Click the check mark to the right of each data type that you create to save your changes.

Figure 54. Adding nested data types with constraints

To change the order or nesting of data types, you can click the left end of the data type row and drag the row as needed:

Figure 55. Dragging data types to change data type order or nesting
Return to the decision table and, for each column, 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 56. Decision table for loan prequalification

For boxed expression types other than decision tables, you follow these guidelines similarly to navigate the boxed expression tables and define custom data types as needed.

For example, the following boxed function expression uses custom tCandidate and tProfile structured data types to associate data for online dating compatibility:

Figure 57. Boxed function expression for online dating compatibility

Figure 58. Custom data type definitions for online dating compatibility

Figure 59. Parameter definitions with custom data types for online dating compatibility

Included models in DMN files in Business Central

In the DMN designer in Business Central, you can use the Included Models tab to include other DMN models and Predictive Model Markup Language (PMML) models from your project in a specified DMN file. When you include a DMN model within another DMN file, you can use all of the nodes and logic from both models in the same decision requirements diagram (DRD). When you include a PMML model within a DMN file, you can invoke that PMML model as a boxed function expression for a DMN decision node or business knowledge model node.

You cannot include DMN or PMML models from other projects in Business Central.

Including other DMN models within a DMN file in Business Central

In Business Central, you can include other DMN models from your project in a specified DMN file. When you include a DMN model within another DMN file, you can use all of the nodes and logic from both models in the same decision requirements diagram (DRD), but you cannot edit the nodes from the included model. To edit nodes from included models, you must update the source file for the included model directly. If you update the source file for an included DMN model, open the DMN file where the DMN model is included (or close an re-open) to verify the changes.

You cannot include DMN models from other projects in Business Central.

Prerequisites

The DMN models are created or imported (as .dmn files) in the same project in Business Central as the DMN file in which you want to include the models.

Procedure

In Business Central, go to Menu → Design → Projects, click the project name, and select the DMN file you want to modify.
In the DMN designer, click the Included Models tab.
Click Include Model, select a DMN model from your project in the Models list, enter a unique name for the included model, and click Include:

Figure 60. Including a DMN model

The DMN model is added to this DMN file, and all DRD nodes from the included model are listed under Decision Components in the Decision Navigator view:

Figure 61. DMN file with decision components from the included DMN model

All data types from the included model are also listed in read-only mode in the Data Types tab for the DMN file:

Figure 62. DMN file with data types from the included DMN model
In the Model tab of the DMN designer, click and drag the included DRD components onto the canvas to begin implementing them in your DRD:

Figure 63. Adding DRD components from the included DMN model

To edit DRD nodes or data types from included models, you must update the source file for the included model directly. If you update the source file for an included DMN model, open the DMN file where the DMN model is included (or close an re-open) to verify the changes.

To edit the included model name or to remove the included model from the DMN file, use the Included Models tab in the DMN designer.

Important
When you remove an included model, any nodes from that included model that are currently used in the DRD are also removed.

Including PMML models within a DMN file in Business Central

In Business Central, you can include Predictive Model Markup Language (PMML) models from your project in a specified DMN file. When you include a PMML model within a DMN file, you can invoke that PMML model as a boxed function expression for a DMN decision node or business knowledge model node. If you update the source file for an included PMML model, you must remove and re-include the PMML model in the DMN file to apply the source changes.

You cannot include PMML models from other projects in Business Central.

Prerequisites

The PMML models are imported (as .pmml files) in the same project in Business Central as the DMN file in which you want to include the models.

Procedure

In your DMN project, add the following dependencies to the project pom.xml file to enable PMML evaluation:

<!-- Required for the PMML compiler -->
<dependency>
  <groupId>org.drools</groupId>
  <artifactId>kie-pmml</artifactId>
  <version>${bamoe.version}</version>
  <scope>provided</scope>
</dependency>

<!-- Alternative dependencies for JPMML Evaluator, override `kie-pmml` dependency -->
<dependency>
  <groupId>org.kie</groupId>
  <artifactId>kie-dmn-jpmml</artifactId>
  <version>${bamoe.version}</version>
  <scope>provided</scope>
</dependency>
<dependency>
  <groupId>org.jpmml</groupId>
  <artifactId>pmml-evaluator</artifactId>
  <version>1.5.1</version>
  <scope>provided</scope>
</dependency>
<dependency>
  <groupId>org.jpmml</groupId>
  <artifactId>pmml-evaluator-extension</artifactId>
  <version>1.5.1</version>
  <scope>provided</scope>
</dependency>

To access the project pom.xml file in Business Central, you can select any existing asset in the project and then in the Project Explorer menu on the left side of the screen, click the Customize View gear icon and select Repository View → pom.xml.

If you want to use the full PMML specification implementation with the Java Evaluator API for PMML (JPMML), use the alternative set of JPMML dependencies in your DMN project. If the JPMML dependencies and the standard kie-pmml dependency are both present, the kie-pmml dependency is disabled. For information about JPMML licensing terms, see Openscoring.io.

Important

The legacy kie-pmml dependency is deprecated with IBM Business Automation Manager Open Editions 7.10.0 and will be replaced by kie-pmml-trusty dependency in a future IBM Business Automation Manager Open Editions release.

Note

Instead of specifying an IBM Business Automation Manager Open Editions <version> for individual dependencies, consider adding the IBM Business Automation Manager Open Editions bill of materials (BOM) dependency to dependencyManagement section of your project pom.xml file. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

Example BOM dependency:

<dependency>
  <groupId>com.redhat.ba</groupId>
  <artifactId>ba-platform-bom</artifactId>
  <version>8.0.4.redhat-00001</version>
  <scope>import</scope>
  <type>pom</type>
</dependency>

For more information about the IBM Business Automation Manager Open Editions BOM, see What is the mapping between RHPAM product and maven library version?. What is the mapping between RHDM product and maven library version?.

If you added the JPMML dependencies in your DMN project to use the JPMML Evaluator, download the following JAR files and add them to the ~/kie-server.war/WEB-INF/lib and ~/business-central.war/WEB-INF/lib directories in your IBM Business Automation Manager Open Editions distribution:

kie-dmn-jpmml JAR file in the IBM Business Automation Manager Open Editions 8.0 - Maven Repository distribution (bamoe-8.0.4-maven-repository/maven-repository/org/kie/kie-dmn-jpmml/7.67.2.Final-redhat-0017/kie-dmn-jpmml-7.67.2.Final-redhat-0017.jar) from the IBM Support page.

These artifacts are required to enable JPMML evaluation in KIE Server and Business Central.

Important

IBM supports integration with the Java Evaluator API for PMML (JPMML) for PMML execution in IBM Business Automation Manager Open Editions. However, IBM does not support the JPMML libraries directly. If you include JPMML libraries in your IBM Business Automation Manager Open Editions distribution, see the Openscoring.io licensing terms for JPMML.

In Business Central, go to Menu → Design → Projects, click the project name, and select the DMN file you want to modify.
In the DMN designer, click the Included Models tab.
Click Include Model, select a PMML model from your project in the Models list, enter a unique name for the included model, and click Include:

Figure 64. Including a PMML model

The PMML model is added to this DMN file:

Figure 65. DMN file with included PMML model
In the Model tab of the DMN designer, select or create the decision node or business knowledge model node in which you want to invoke the PMML model and click the Edit icon to open the DMN boxed expression designer:

Figure 66. Opening a new decision node boxed expression

Figure 67. Opening a new business knowledge model boxed expression
Set the expression type to Function (default for business knowledge model nodes), click the top-left function cell, and select PMML.
In the document and model rows in the table, double-click the undefined cells to specify the included PMML document and the relevant PMML model within that document:

Figure 68. Adding a PMML model in a DMN business knowledge model

Figure 69. Example PMML definition in a DMN business knowledge model

If you update the source file for an included PMML model, you must remove and re-include the PMML model in the DMN file to apply the source changes.

To edit the included model name or to remove the included model from the DMN file, use the Included Models tab in the DMN designer.

Creating DMN models with multiple diagrams in Business Central

For complex DMN models, you can use the DMN designer in Business Central to design multiple DMN decision requirements diagrams (DRDs) that represent parts of the overall decision requirements graph (DRG) for the DMN decision model. In simple cases, you can use a single DRD to represent all of the overall DRG for the decision model, but in complex cases, a single DRD can become large and difficult to follow. Therefore, to better organize DMN decision models with many decision requirements, you can divide the model into smaller nested DRDs that constitute the larger central DRD representation of the overall DRG.

Prerequisites

You understand how to design DRDs in Business Central. For information about creating DRDs, see Creating and editing DMN models in Business Central.

Procedure

In Business Central, navigate to your DMN project and create or import a DMN file in the project.
Open the new or imported DMN file to view the DRD in the DMN designer, and begin designing or modifying the DRD using the DMN nodes in the left toolbar.
For any DMN nodes that you want to define in a separate nested DRD, select the node, click the DRD Actions icon, and select from the available options.
Figure 70. DRD actions icon for subdividing a DRD

The following options are available:
- Create: Use this option to create a nested DRD where you can separately define the DMN components and diagram for the selected node.
- Add to: If you already created a nested DRD, use this option to add the selected node to an existing DRD.
- Remove: If the node that you selected is already within a nested DRD, use this option to remove the node from that nested DRD.
After you create a nested DRD within your DMN decision model, the new DRD opens in a separate DRD canvas and the available DRD and components are listed in the Decision Navigator left menu. You can use the Decision Navigator menu to rename or remove a nested DRD.

Figure 71. Rename new nested DRD in the Decision Navigator menu
In the separate canvas for the new nested DRD, design the flow and logic for all required components in this portion of the DMN model, as usual.
Continue adding and defining any other nested DRDs for your decision model and save the completed DMN file.

For example, the following DRD for a loan prequalification decision model contains all DMN components for the model without any nested DRDs. This example relies on the single DRD for all components and logic, resulting in a large and complex diagram.

Figure 72. Single DRD for loan prequalification

Alternatively, by following the steps in this procedure, you can divide this example DRD into multiple nested DRDs to better organize the decision requirements, as shown in the following example:

Figure 73. Multiple nested DRDs for loan prequalification

Figure 74. Overview of front end ratio DRD

Figure 75. DRD for front end ratio

Figure 76. Overview of credit score rating DRD

Figure 77. DRD for credit score rating

Figure 78. Overview of back end ratio DRD

Figure 79. DRD for back end ratio

DMN model documentation in Business Central

In the DMN designer in Business Central, you can use the Documentation tab to generate a report of your DMN model that you can print or download as an HTML file for offline use. The DMN model report contains all decision requirements diagrams (DRDs), data types, and boxed expressions in your DMN model. You can use this report to share your DMN model details or as part of your internal reporting workflow.

Figure 80. Example DMN model report

DMN designer navigation and properties in Business Central

The DMN designer in Business Central provides the following additional features to help you navigate through the components and properties of decision requirements diagrams (DRDs).

DMN file and diagram views

In the upper-left corner of the DMN designer, select the Project Explorer view to navigate between all DMN and other files or select the Decision Navigator view to navigate between the decision components, graphs, and boxed expressions of a selected DRD:

Figure 81. Project Explorer view

Figure 82. Decision Navigator view

Note	The DRD components from any DMN models included in the DMN file (in the Included Models tab) are also listed in the Decision Components panel for the DMN file.

In the upper-right corner of the DMN designer, select the Explore diagram icon to view an elevated preview of the selected DRD and to navigate between the nodes of the selected DRD:

Figure 83. Explore diagram view

DRD properties and design

In the upper-right corner of the DMN designer, select the Properties icon to modify the identifying information, data types, and appearance of a selected DRD, DRD node, or boxed expression cell:

Figure 84. DRD node properties

To view the properties of the entire DRD, click the DRD canvas background instead of a specific node.

DRD search

In the upper-right corner of the DMN designer, use the search bar to search for text that appears in your DRD. The search feature is especially helpful in complex DRDs with many nodes:

Figure 85. DRD search

DMN decision service details

Select a decision service node in the DMN designer to view additional properties, including Input Data, Encapsulated Decisions, and Output Decisions in the Properties panel.

Figure 86. Decision Service details

DMN model execution

You can create or import DMN files in your IBM Business Automation Manager Open Editions project using Business Central or package the DMN files as part of your project knowledge JAR (KJAR) file without Business Central. After you implement your DMN files in your IBM Business Automation Manager Open Editions project, you can execute the DMN decision service by deploying the KIE container that contains it to KIE Server for remote access or by manipulating the KIE container directly as a dependency of the calling application. Other options for creating and deploying DMN knowledge packages are also available, and most are similar for all types of knowledge assets, such as DRL files or process definitions.

For information about including external DMN assets with your project packaging and deployment method, see Packaging and deploying an IBM Business Automation Manager Open Editions project.

Embedding a DMN call directly in a Java application

A KIE container is local when the knowledge assets are either embedded directly into the calling program or are physically pulled in using Maven dependencies for the KJAR. You typically embed knowledge assets directly into a project if there is a tight relationship between the version of the code and the version of the DMN definition. Any changes to the decision take effect after you have intentionally updated and redeployed the application. A benefit of this approach is that proper operation does not rely on any external dependencies to the run time, which can be a limitation of locked-down environments.

Using Maven dependencies enables further flexibility because the specific version of the decision can dynamically change, (for example, by using a system property), and it can be periodically scanned for updates and automatically updated. This introduces an external dependency on the deploy time of the service, but executes the decision locally, reducing reliance on an external service being available during run time.

Prerequisites

You have built the DMN project as a KJAR artifact and deployed it to a Maven repository, or you have included your DMN assets as part of your project classpath:
mvn clean install
For more information about project packaging and deployment and executable models, see Packaging and deploying an IBM Business Automation Manager Open Editions project.

Procedure

In your client application, add the following dependencies to the relevant classpath of your Java project:

<!-- Required for the DMN runtime API -->
<dependency>
  <groupId>org.kie</groupId>
  <artifactId>kie-dmn-core</artifactId>
  <version>${bamoe.version}</version>
</dependency>

<!-- Required if not using classpath KIE container -->
<dependency>
  <groupId>org.kie</groupId>
  <artifactId>kie-ci</artifactId>
  <version>${bamoe.version}</version>
</dependency>

The <version> is the Maven artifact version for IBM Business Automation Manager Open Editions currently used in your project (for example, 7.67.2.Final-redhat-0017).

Note

Instead of specifying an IBM Business Automation Manager Open Editions <version> for individual dependencies, consider adding the IBM Business Automation Manager Open Editions bill of materials (BOM) dependency to your project pom.xml file. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

Example BOM dependency:

<dependency>
  <groupId>com.redhat.ba</groupId>
  <artifactId>ba-platform-bom</artifactId>
  <version>8.0.4.redhat-00001</version>
  <scope>import</scope>
  <type>pom</type>
</dependency>

Create a KIE container from classpath or ReleaseId:

KieServices kieServices = KieServices.Factory.get();

ReleaseId releaseId = kieServices.newReleaseId( "org.acme", "my-kjar", "1.0.0" );
KieContainer kieContainer = kieServices.newKieContainer( releaseId );

Alternative option:

KieServices kieServices = KieServices.Factory.get();

KieContainer kieContainer = kieServices.getKieClasspathContainer();

Obtain DMNRuntime from the KIE container and a reference to the DMN model to be evaluated, by using the model namespace and modelName:

DMNRuntime dmnRuntime = KieRuntimeFactory.of(kieContainer.getKieBase()).get(DMNRuntime.class);

String namespace = "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a";
String modelName = "dmn-movieticket-ageclassification";

DMNModel dmnModel = dmnRuntime.getModel(namespace, modelName);

Execute the decision services for the desired model:

DMNContext dmnContext = dmnRuntime.newContext();  // (1)

for (Integer age : Arrays.asList(1,12,13,64,65,66)) {
    dmnContext.set("Age", age);  // (2)
    DMNResult dmnResult =
        dmnRuntime.evaluateAll(dmnModel, dmnContext);  // (3)

    for (DMNDecisionResult dr : dmnResult.getDecisionResults()) {  // (4)
        log.info("Age: " + age + ", " +
                 "Decision: '" + dr.getDecisionName() + "', " +
                 "Result: " + dr.getResult());
  }
}

Instantiate a new DMN Context to be the input for the model evaluation. Note that this example is looping through the Age Classification decision multiple times.
Assign input variables for the input DMN context.
Evaluate all DMN decisions defined in the DMN model.
Each evaluation may result in one or more results, creating the loop.

This example prints the following output:

Age 1 Decision 'AgeClassification' : Child
Age 12 Decision 'AgeClassification' : Child
Age 13 Decision 'AgeClassification' : Adult
Age 64 Decision 'AgeClassification' : Adult
Age 65 Decision 'AgeClassification' : Senior
Age 66 Decision 'AgeClassification' : Senior

If the DMN model was not previously compiled as an executable model for more efficient execution, you can enable the following property when you execute your DMN models:

-Dorg.kie.dmn.compiler.execmodel=true

Executing a DMN service using the KIE Server Java client API

The KIE Server Java client API provides a lightweight approach to invoking a remote DMN service either through the REST or JMS interfaces of KIE Server. This approach reduces the number of runtime dependencies necessary to interact with a KIE base. Decoupling the calling code from the decision definition also increases flexibility by enabling them to iterate independently at the appropriate pace.

For more information about the KIE Server Java client API, see Interacting with IBM Business Automation Manager Open Editions using KIE APIs.

Prerequisites

KIE Server is installed and configured, including a known user name and credentials for a user with the kie-server role. For installation options, see Planning an IBM Business Automation Manager Open Editions installation.
You have built the DMN project as a KJAR artifact and deployed it to KIE Server:
mvn clean install
For more information about project packaging and deployment and executable models, see Packaging and deploying an IBM Business Automation Manager Open Editions project.
You have the ID of the KIE container containing the DMN model. If more than one model is present, you must also know the model namespace and model name of the relevant model.

Procedure

In your client application, add the following dependency to the relevant classpath of your Java project:

<!-- Required for the KIE Server Java client API -->
<dependency>
  <groupId>org.kie.server</groupId>
  <artifactId>kie-server-client</artifactId>
  <version>${bamoe.version}</version>
</dependency>

The <version> is the Maven artifact version for IBM Business Automation Manager Open Editions currently used in your project (for example, 7.67.2.Final-redhat-0017).

Note

Instead of specifying an IBM Business Automation Manager Open Editions <version> for individual dependencies, consider adding the IBM Business Automation Manager Open Editions bill of materials (BOM) dependency to your project pom.xml file. When you add the BOM files, the correct versions of transitive dependencies from the provided Maven repositories are included in the project.

Example BOM dependency:

<dependency>
  <groupId>com.redhat.ba</groupId>
  <artifactId>ba-platform-bom</artifactId>
  <version>8.0.4.redhat-00001</version>
  <scope>import</scope>
  <type>pom</type>
</dependency>

Instantiate a KieServicesClient instance with the appropriate connection information.
Example:
KieServicesConfiguration conf = KieServicesFactory.newRestConfiguration(URL, USER, PASSWORD); // (1) conf.setMarshallingFormat(MarshallingFormat.JSON); // (2) KieServicesClient kieServicesClient = KieServicesFactory.newKieServicesClient(conf);
1. The connection information:
  
  Example URL: http://localhost:8080/kie-server/services/rest/server
  
  The credentials should reference a user with the kie-server role.
2. The Marshalling format is an instance of org.kie.server.api.marshalling.MarshallingFormat. It controls whether the messages will be JSON or XML. Options for Marshalling format are JSON, JAXB, or XSTREAM.
Obtain a DMNServicesClient from the KIE server Java client connected to the related KIE Server by invoking the method getServicesClient() on the KIE server Java client instance:
```
DMNServicesClient dmnClient = kieServicesClient.getServicesClient(DMNServicesClient.class );
```
The dmnClient can now execute decision services on KIE Server.
Execute the decision services for the desired model.
Example:
for (Integer age : Arrays.asList(1,12,13,64,65,66)) { DMNContext dmnContext = dmnClient.newContext(); // (1) dmnContext.set("Age", age); // (2) ServiceResponse<DMNResult> serverResp = // (3) dmnClient.evaluateAll($kieContainerId, $modelNamespace, $modelName, dmnContext); DMNResult dmnResult = serverResp.getResult(); // (4) for (DMNDecisionResult dr : dmnResult.getDecisionResults()) { log.info("Age: " + age + ", " + "Decision: '" + dr.getDecisionName() + "', " + "Result: " + dr.getResult()); } }
1. Instantiate a new DMN Context to be the input for the model evaluation. Note that this example is looping through the Age Classification decision multiple times.
2. Assign input variables for the input DMN Context.
3. Evaluate all the DMN Decisions defined in the DMN model:
  
  $kieContainerId is the ID of the container where the KJAR containing the DMN model is deployed
  
  $modelNamespace is the namespace for the model.
  
  $modelName is the name for the model.
4. The DMN Result object is available from the server response.
At this point, the dmnResult contains all the decision results from the evaluated DMN model.

You can also execute only a specific DMN decision in the model by using alternative methods of the DMNServicesClient.

Note
If the KIE container only contains one DMN model, you can omit $modelNamespace and $modelName because the KIE Server API selects it by default.

Executing a DMN service using the KIE Server REST API

Directly interacting with the REST endpoints of KIE Server provides the most separation between the calling code and the decision logic definition. The calling code is completely free of direct dependencies, and you can implement it in an entirely different development platform such as Node.js or .NET. The examples in this section demonstrate Nix-style curl commands but provide relevant information to adapt to any REST client.

When you use a REST endpoint of KIE Server, the best practice is to define a domain object POJO Java class, annotated with standard KIE Server marshalling annotations. For example, the following code is using a domain object Person class that is annotated properly:

Example POJO Java class

@javax.xml.bind.annotation.XmlAccessorType(javax.xml.bind.annotation.XmlAccessType.FIELD)
public class Person implements java.io.Serializable {

    static final long serialVersionUID = 1L;

    private java.lang.String id;
    private java.lang.String name;
    @javax.xml.bind.annotation.adapters.XmlJavaTypeAdapter(org.kie.internal.jaxb.LocalDateXmlAdapter.class)
    private java.time.LocalDate dojoining;

    public Person() {
    }

    public java.lang.String getId() {
        return this.id;
    }

    public void setId(java.lang.String id) {
        this.id = id;
    }

    public java.lang.String getName() {
        return this.name;
    }

    public void setName(java.lang.String name) {
        this.name = name;
    }

    public java.time.LocalDate getDojoining() {
        return this.dojoining;
    }

    public void setDojoining(java.time.LocalDate dojoining) {
        this.dojoining = dojoining;
    }

    public Person(java.lang.String id, java.lang.String name,
            java.time.LocalDate dojoining) {
        this.id = id;
        this.name = name;
        this.dojoining = dojoining;
    }

}

For more information about the KIE Server REST API, see Interacting with IBM Business Automation Manager Open Editions using KIE APIs.

Prerequisites

KIE Server is installed and configured, including a known user name and credentials for a user with the kie-server role. For installation options, see Planning an IBM Business Automation Manager Open Editions installation.
You have built the DMN project as a KJAR artifact and deployed it to KIE Server:
mvn clean install
For more information about project packaging and deployment and executable models, see Packaging and deploying an IBM Business Automation Manager Open Editions project.
You have the ID of the KIE container containing the DMN model. If more than one model is present, you must also know the model namespace and model name of the relevant model.

Procedure

Determine the base URL for accessing the KIE Server REST API endpoints. This requires knowing the following values (with the default local deployment values as an example):
- Host (localhost)
- Port (8080)
- Root context (kie-server)
- Base REST path (services/rest/)
Example base URL in local deployment:

http://localhost:8080/kie-server/services/rest/
Determine user authentication requirements.

When users are defined directly in the KIE Server configuration, HTTP Basic authentication is used and requires the user name and password. Successful requests require that the user have the kie-server role.

The following example demonstrates how to add credentials to a curl request:
```
curl -u username:password <request>
```
If KIE Server is configured with Red Hat Single Sign-On, the request must include a bearer token:
```
curl -H "Authorization: bearer $TOKEN" <request>
```
Specify the format of the request and response. The REST API endpoints work with both JSON and XML formats and are set using request headers:
JSON
```
curl -H "accept: application/json" -H "content-type: application/json"
```
XML
```
curl -H "accept: application/xml" -H "content-type: application/xml"
```

Optional: Query the container for a list of deployed decision models:

[GET] server/containers/{containerId}/dmn

Example curl request:

curl -u krisv:krisv -H "accept: application/xml" -X GET "http://localhost:8080/kie-server/services/rest/server/containers/MovieDMNContainer/dmn"

Sample XML output:

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<response type="SUCCESS" msg="OK models successfully retrieved from container 'MovieDMNContainer'">
    <dmn-model-info-list>
        <model>
            <model-namespace>http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a</model-namespace>
            <model-name>dmn-movieticket-ageclassification</model-name>
            <model-id>_99</model-id>
            <decisions>
                <dmn-decision-info>
                    <decision-id>_3</decision-id>
                    <decision-name>AgeClassification</decision-name>
                </dmn-decision-info>
            </decisions>
        </model>
    </dmn-model-info-list>
</response>

Sample JSON output:

{
  "type" : "SUCCESS",
  "msg" : "OK models successfully retrieved from container 'MovieDMNContainer'",
  "result" : {
    "dmn-model-info-list" : {
      "models" : [ {
        "model-namespace" : "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a",
        "model-name" : "dmn-movieticket-ageclassification",
        "model-id" : "_99",
        "decisions" : [ {
          "decision-id" : "_3",
          "decision-name" : "AgeClassification"
        } ]
      } ]
    }
  }
}

Execute the model:

[POST] server/containers/{containerId}/dmn

Example curl request:

curl -u krisv:krisv -H "accept: application/json" -H "content-type: application/json" -X POST "http://localhost:8080/kie-server/services/rest/server/containers/MovieDMNContainer/dmn" -d "{ \"model-namespace\" : \"http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a\", \"model-name\" : \"dmn-movieticket-ageclassification\", \"decision-name\" : [ ], \"decision-id\" : [ ], \"dmn-context\" : {\"Age\" : 66}}"

Example JSON request:

{
  "model-namespace" : "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a",
  "model-name" : "dmn-movieticket-ageclassification",
  "decision-name" : [ ],
  "decision-id" : [ ],
  "dmn-context" : {"Age" : 66}
}

Example XML request (JAXB format):

<?xml version="1.0" encoding="UTF-8"?>
<dmn-evaluation-context>
    <model-namespace>http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a</model-namespace>
    <model-name>dmn-movieticket-ageclassification</model-name>
    <dmn-context xsi:type="jaxbListWrapper" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
        <type>MAP</type>
        <element xsi:type="jaxbStringObjectPair" key="Age">
            <value xsi:type="xs:int" xmlns:xs="http://www.w3.org/2001/XMLSchema">66</value>
        </element>
    </dmn-context>
</dmn-evaluation-context>

Note	Regardless of the request format, the request requires the following elements: Model namespace Model name Context object containing input values

Example JSON response:

{
  "type" : "SUCCESS",
  "msg" : "OK from container 'MovieDMNContainer'",
  "result" : {
    "dmn-evaluation-result" : {
      "messages" : [ ],
      "model-namespace" : "http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a",
      "model-name" : "dmn-movieticket-ageclassification",
      "decision-name" : [ ],
      "dmn-context" : {
        "Age" : 66,
        "AgeClassification" : "Senior"
      },
      "decision-results" : {
        "_3" : {
          "messages" : [ ],
          "decision-id" : "_3",
          "decision-name" : "AgeClassification",
          "result" : "Senior",
          "status" : "SUCCEEDED"
        }
      }
    }
  }
}

Example XML (JAXB format) response:

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<response type="SUCCESS" msg="OK from container 'MovieDMNContainer'">
      <dmn-evaluation-result>
            <model-namespace>http://www.redhat.com/_c7328033-c355-43cd-b616-0aceef80e52a</model-namespace>
            <model-name>dmn-movieticket-ageclassification</model-name>
            <dmn-context xsi:type="jaxbListWrapper" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
                  <type>MAP</type>
                  <element xsi:type="jaxbStringObjectPair" key="Age">
                        <value xsi:type="xs:int" xmlns:xs="http://www.w3.org/2001/XMLSchema">66</value>
                  </element>
                  <element xsi:type="jaxbStringObjectPair" key="AgeClassification">
                        <value xsi:type="xs:string" xmlns:xs="http://www.w3.org/2001/XMLSchema">Senior</value>
                  </element>
            </dmn-context>
            <messages/>
            <decisionResults>
                  <entry>
                        <key>_3</key>
                        <value>
                              <decision-id>_3</decision-id>
                              <decision-name>AgeClassification</decision-name>
                              <result xsi:type="xs:string" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Senior</result>
                              <messages/>
                              <status>SUCCEEDED</status>
                        </value>
                  </entry>
            </decisionResults>
      </dmn-evaluation-result>
</response>

REST endpoints for specific DMN models

IBM Business Automation Manager Open Editions provides model-specific DMN KIE Server endpoints that you can use to interact with your specific DMN model without using the Business Central user interface.

For each DMN model in a container in IBM Business Automation Manager Open Editions, the following KIE Server REST endpoints are automatically generated based on the content of the DMN model:

POST /server/containers/{containerId}/dmn/models/{modelname}: A business-domain endpoint for evaluating a specified DMN model in a container
POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}: A business-domain endpoint for evaluating a specified decision service component in a specific DMN model available in a container
POST /server/containers/{containerId}/dmn/models/{modelname}/dmnresult: An endpoint for evaluating a specified DMN model containing customized body payload and returning a DMNResult response, including business-domain context, helper messages, and helper decision pointers
POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}/dmnresult: An endpoint for evaluating a specified decision service component in a specific DMN model and returning a DMNResult response, including the business-domain context, helper messages, and help decision pointers for the decision service
GET /server/containers/{containerId}/dmn/models/{modelname}: An endpoint for returning standard DMN XML without decision logic and containing the inputs and decisions of the specified DMN model
GET /server/containers/{containerId}/dmn/openapi.json (|.yaml): An endpoint for retrieving Swagger or OAS for the DMN models in a specified container

You can use these endpoints to interact with a DMN model or a specific decision service within a model. As you decide between using business-domain and dmnresult variants of these REST endpoints, review the following considerations:

REST business-domain endpoints: Use this endpoint type if a client application is only concerned with a positive evaluation outcome, is not interested in parsing Info or Warn messages, and only needs an HTTP 5xx response for any errors. This type of endpoint is also helpful for single-page application-like clients, due to singleton coercion of decision service results that resemble the DMN modeling behavior.
REST dmnresult endpoints: Use this endpoint type if a client needs to parse Info, Warn, or Error messages in all cases.

For each endpoint, use a REST client or curl utility to send requests with the following components:

Base URL: http://HOST:PORT/kie-server/services/rest/
Path parameters:
- {containerId}: The string identifier of the container, such as mykjar-project
- {modelName}: The string identifier of the DMN model, such as Traffic Violation
- {decisionServiceName}: The string identifier of the decision service component in the DMN DRG, such as TrafficViolationDecisionService
- dmnresult: The string identifier that enables the endpoint to return a full DMNResult response with more detailed Info, Warn, and Error messaging
HTTP headers: For POST requests only:
- accept: application/json
- content-type: application/json
HTTP methods: GET or POST

The examples in the following endpoints are based on a mykjar-project container that contains a Traffic Violation DMN model, containing a TrafficViolationDecisionService decision service component.

For all of these endpoints, if a DMN evaluation Error message occurs, a DMNResult response is returned along with an HTTP 5xx error. If a DMN Info or Warn message occurs, the relevant response is returned along with the business-domain REST body, in the X-Kogito-decision-messages extended HTTP header, to be used for client-side business logic. When there is a requirement of more refined client-side business logic, the client can use the dmnresult variant of the endpoints.

Retrieve Swagger or OAS for DMN models in a specified container

GET /server/containers/{containerId}/dmn/openapi.json (|.yaml)

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/openapi.json (|.yaml)

Return the DMN XML without decision logic

GET /server/containers/{containerId}/dmn/models/{modelname}

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation

Example curl request

curl -u wbadmin:wbadmin -X GET "http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic%20Violation" -H  "accept: application/xml"

Example response (XML)

<?xml version='1.0' encoding='UTF-8'?>
<dmn:definitions xmlns:dmn="http://www.omg.org/spec/DMN/20180521/MODEL/" xmlns="https://kiegroup.org/dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF" xmlns:di="http://www.omg.org/spec/DMN/20180521/DI/" xmlns:kie="http://www.drools.org/kie/dmn/1.2" xmlns:feel="http://www.omg.org/spec/DMN/20180521/FEEL/" xmlns:dmndi="http://www.omg.org/spec/DMN/20180521/DMNDI/" xmlns:dc="http://www.omg.org/spec/DMN/20180521/DC/" id="_1C792953-80DB-4B32-99EB-25FBE32BAF9E" name="Traffic Violation" expressionLanguage="http://www.omg.org/spec/DMN/20180521/FEEL/" typeLanguage="http://www.omg.org/spec/DMN/20180521/FEEL/" namespace="https://kiegroup.org/dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF">
  <dmn:extensionElements/>
  <dmn:itemDefinition id="_63824D3F-9173-446D-A940-6A7F0FA056BB" name="tDriver" isCollection="false">
    <dmn:itemComponent id="_9DAB5DAA-3B44-4F6D-87F2-95125FB2FEE4" name="Name" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_856BA8FA-EF7B-4DF9-A1EE-E28263CE9955" name="Age" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_FDC2CE03-D465-47C2-A311-98944E8CC23F" name="State" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_D6FD34C4-00DC-4C79-B1BF-BBCF6FC9B6D7" name="City" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_7110FE7E-1A38-4C39-B0EB-AEEF06BA37F4" name="Points" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_40731093-0642-4588-9183-1660FC55053B" name="tViolation" isCollection="false">
    <dmn:itemComponent id="_39E88D9F-AE53-47AD-B3DE-8AB38D4F50B3" name="Code" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_1648EA0A-2463-4B54-A12A-D743A3E3EE7B" name="Date" isCollection="false">
      <dmn:typeRef>date</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_9F129EAA-4E71-4D99-B6D0-84EEC3AC43CC" name="Type" isCollection="false">
      <dmn:typeRef>string</dmn:typeRef>
      <dmn:allowedValues kie:constraintType="enumeration" id="_626A8F9C-9DD1-44E0-9568-0F6F8F8BA228">
        <dmn:text>"speed", "parking", "driving under the influence"</dmn:text>
      </dmn:allowedValues>
    </dmn:itemComponent>
    <dmn:itemComponent id="_DDD10D6E-BD38-4C79-9E2F-8155E3A4B438" name="Speed Limit" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_229F80E4-2892-494C-B70D-683ABF2345F6" name="Actual Speed" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:itemDefinition id="_2D4F30EE-21A6-4A78-A524-A5C238D433AE" name="tFine" isCollection="false">
    <dmn:itemComponent id="_B9F70BC7-1995-4F51-B949-1AB65538B405" name="Amount" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
    <dmn:itemComponent id="_F49085D6-8F08-4463-9A1A-EF6B57635DBD" name="Points" isCollection="false">
      <dmn:typeRef>number</dmn:typeRef>
    </dmn:itemComponent>
  </dmn:itemDefinition>
  <dmn:inputData id="_1929CBD5-40E0-442D-B909-49CEDE0101DC" name="Violation">
    <dmn:variable id="_C16CF9B1-5FAB-48A0-95E0-5FCD661E0406" name="Violation" typeRef="tViolation"/>
  </dmn:inputData>
  <dmn:decision id="_4055D956-1C47-479C-B3F4-BAEB61F1C929" name="Fine">
    <dmn:variable id="_8C1EAC83-F251-4D94-8A9E-B03ACF6849CD" name="Fine" typeRef="tFine"/>
    <dmn:informationRequirement id="_800A3BBB-90A3-4D9D-BA5E-A311DED0134F">
      <dmn:requiredInput href="#_1929CBD5-40E0-442D-B909-49CEDE0101DC"/>
    </dmn:informationRequirement>
  </dmn:decision>
  <dmn:inputData id="_1F9350D7-146D-46F1-85D8-15B5B68AF22A" name="Driver">
    <dmn:variable id="_A80F16DF-0DB4-43A2-B041-32900B1A3F3D" name="Driver" typeRef="tDriver"/>
  </dmn:inputData>
  <dmn:decision id="_8A408366-D8E9-4626-ABF3-5F69AA01F880" name="Should the driver be suspended?">
    <dmn:question>Should the driver be suspended due to points on his license?</dmn:question>
    <dmn:allowedAnswers>"Yes", "No"</dmn:allowedAnswers>
    <dmn:variable id="_40387B66-5D00-48C8-BB90-E83EE3332C72" name="Should the driver be suspended?" typeRef="string"/>
    <dmn:informationRequirement id="_982211B1-5246-49CD-BE85-3211F71253CF">
      <dmn:requiredInput href="#_1F9350D7-146D-46F1-85D8-15B5B68AF22A"/>
    </dmn:informationRequirement>
    <dmn:informationRequirement id="_AEC4AA5F-50C3-4FED-A0C2-261F90290731">
      <dmn:requiredDecision href="#_4055D956-1C47-479C-B3F4-BAEB61F1C929"/>
    </dmn:informationRequirement>
  </dmn:decision>
  <dmndi:DMNDI>
    <dmndi:DMNDiagram>
      <di:extension/>
      <dmndi:DMNShape id="dmnshape-_1929CBD5-40E0-442D-B909-49CEDE0101DC" dmnElementRef="_1929CBD5-40E0-442D-B909-49CEDE0101DC" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="708" y="350" width="100" height="50"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNShape id="dmnshape-_4055D956-1C47-479C-B3F4-BAEB61F1C929" dmnElementRef="_4055D956-1C47-479C-B3F4-BAEB61F1C929" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="709" y="210" width="100" height="50"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNShape id="dmnshape-_1F9350D7-146D-46F1-85D8-15B5B68AF22A" dmnElementRef="_1F9350D7-146D-46F1-85D8-15B5B68AF22A" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="369" y="344" width="100" height="50"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNShape id="dmnshape-_8A408366-D8E9-4626-ABF3-5F69AA01F880" dmnElementRef="_8A408366-D8E9-4626-ABF3-5F69AA01F880" isCollapsed="false">
        <dmndi:DMNStyle>
          <dmndi:FillColor red="255" green="255" blue="255"/>
          <dmndi:StrokeColor red="0" green="0" blue="0"/>
          <dmndi:FontColor red="0" green="0" blue="0"/>
        </dmndi:DMNStyle>
        <dc:Bounds x="534" y="83" width="133" height="63"/>
        <dmndi:DMNLabel/>
      </dmndi:DMNShape>
      <dmndi:DMNEdge id="dmnedge-_800A3BBB-90A3-4D9D-BA5E-A311DED0134F" dmnElementRef="_800A3BBB-90A3-4D9D-BA5E-A311DED0134F">
        <di:waypoint x="758" y="375"/>
        <di:waypoint x="759" y="235"/>
      </dmndi:DMNEdge>
      <dmndi:DMNEdge id="dmnedge-_982211B1-5246-49CD-BE85-3211F71253CF" dmnElementRef="_982211B1-5246-49CD-BE85-3211F71253CF">
        <di:waypoint x="419" y="369"/>
        <di:waypoint x="600.5" y="114.5"/>
      </dmndi:DMNEdge>
      <dmndi:DMNEdge id="dmnedge-_AEC4AA5F-50C3-4FED-A0C2-261F90290731" dmnElementRef="_AEC4AA5F-50C3-4FED-A0C2-261F90290731">
        <di:waypoint x="759" y="235"/>
        <di:waypoint x="600.5" y="114.5"/>
      </dmndi:DMNEdge>
    </dmndi:DMNDiagram>
  </dmndi:DMNDI>

Evaluate a specified DMN model in a specified container

POST /server/containers/{containerId}/dmn/models/{modelname}

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation

Example curl request

curl -u wbadmin:wbadmin-X POST "http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation" -H  "accept: application/json" -H  "Content-Type: application/json" -d "{\"Driver\":{\"Points\":15},\"Violation\":{\"Date\":\"2021-04-08\",\"Type\":\"speed\",\"Actual Speed\":135,\"Speed Limit\":100}}"

Example POST request body with input data

{
  "Driver": {
    "Points": 15
  },
  "Violation": {
    "Date": "2021-04-08",
    "Type": "speed",
    "Actual Speed": 135,
    "Speed Limit": 100
  }
}

Example response (JSON)

{
  "Violation": {
    "Type": "speed",
    "Speed Limit": 100,
    "Actual Speed": 135,
    "Code": null,
    "Date": "2021-04-08"
  },
  "Driver": {
    "Points": 15,
    "State": null,
    "City": null,
    "Age": null,
    "Name": null
  },
  "Fine": {
    "Points": 7,
    "Amount": 1000
  },
  "Should the driver be suspended?": "Yes"
}

Evaluate a specified decision service within a specified DMN model in a container

POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}

For this endpoint, the request body must contain all the requirements of the decision service. The response is the resulting DMN context of the decision service, including the decision values, the original input values, and all other parametric DRG components in serialized form. For example, a business knowledge model is available in string-serialized form in its signature.

If the decision service is composed of a single-output decision, the response is the resulting value of that specific decision. This behavior provides an equivalent value at the API level of a specification feature when invoking the decision service in the model itself. As a result, you can, for example, interact with a DMN decision service from single-page web applications.

Image of decision service in Traffic Violation DMN model

Figure 87. Example TrafficViolationDecisionService decision service with single-output decision

Figure 88. Example TrafficViolationDecisionService decision service with multiple-output decision

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService

Example POST request body with input data

{
  "Driver": {
    "Points": 2
  },
  "Violation": {
    "Type": "speed",
    "Actual Speed": 120,
    "Speed Limit": 100
  }
}

Example curl request

curl -X POST http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService -H 'content-type: application/json' -H 'accept: application/json' -d '{"Driver": {"Points": 2}, "Violation": {"Type": "speed", "Actual Speed": 120, "Speed Limit": 100}}'

Example response for single-output decision (JSON)

"No"

Example response for multiple-output decision (JSON)

{
  "Violation": {
    "Type": "speed",
    "Speed Limit": 100,
    "Actual Speed": 120
  },
  "Driver": {
    "Points": 2
  },
  "Fine": {
    "Points": 3,
    "Amount": 500
  },
  "Should the driver be suspended?": "No"
}

Evaluate a specified DMN model in a specified container and return a DMNResult response

POST /server/containers/{containerId}/dmn/models/{modelname}/dmnresult

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/dmnresult

Example POST request body with input data

{
  "Driver": {
    "Points": 2
  },
  "Violation": {
    "Type": "speed",
    "Actual Speed": 120,
    "Speed Limit": 100
  }
}

Example curl request

curl -X POST http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/dmnresult -H 'content-type: application/json' -H 'accept: application/json' -d '{"Driver": {"Points": 2}, "Violation": {"Type": "speed", "Actual Speed": 120, "Speed Limit": 100}}'

Example response (JSON)

{
  "namespace": "https://kiegroup.org/dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF",
  "modelName": "Traffic Violation",
  "dmnContext": {
    "Violation": {
      "Type": "speed",
      "Speed Limit": 100,
      "Actual Speed": 120,
      "Code": null,
      "Date": null
    },
    "Driver": {
      "Points": 2,
      "State": null,
      "City": null,
      "Age": null,
      "Name": null
    },
    "Fine": {
      "Points": 3,
      "Amount": 500
    },
    "Should the driver be suspended?": "No"
  },
  "messages": [],
  "decisionResults": [
    {
      "decisionId": "_4055D956-1C47-479C-B3F4-BAEB61F1C929",
      "decisionName": "Fine",
      "result": {
        "Points": 3,
        "Amount": 500
      },
      "messages": [],
      "evaluationStatus": "SUCCEEDED"
    },
    {
      "decisionId": "_8A408366-D8E9-4626-ABF3-5F69AA01F880",
      "decisionName": "Should the driver be suspended?",
      "result": "No",
      "messages": [],
      "evaluationStatus": "SUCCEEDED"
    }
  ]
}

Evaluate a specified decision service within a DMN model in a specified container and return a DMNResult response

POST /server/containers/{containerId}/dmn/models/{modelname}/{decisionServiceName}/dmnresult

Example REST endpoint

http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService/dmnresult

Example POST request body with input data

{
  "Driver": {
    "Points": 2
  },
  "Violation": {
    "Type": "speed",
    "Actual Speed": 120,
    "Speed Limit": 100
  }
}

Example curl request

curl -X POST http://localhost:8080/kie-server/services/rest/server/containers/mykjar-project/dmn/models/Traffic Violation/TrafficViolationDecisionService/dmnresult -H 'content-type: application/json' -H 'accept: application/json' -d '{"Driver": {"Points": 2}, "Violation": {"Type": "speed", "Actual Speed": 120, "Speed Limit": 100}}'

Example response (JSON)

{
  "namespace": "https://kiegroup.org/dmn/_A4BCA8B8-CF08-433F-93B2-A2598F19ECFF",
  "modelName": "Traffic Violation",
  "dmnContext": {
    "Violation": {
      "Type": "speed",
      "Speed Limit": 100,
      "Actual Speed": 120,
      "Code": null,
      "Date": null
    },
    "Driver": {
      "Points": 2,
      "State": null,
      "City": null,
      "Age": null,
      "Name": null
    },
    "Should the driver be suspended?": "No"
  },
  "messages": [],
  "decisionResults": [
    {
      "decisionId": "_8A408366-D8E9-4626-ABF3-5F69AA01F880",
      "decisionName": "Should the driver be suspended?",
      "result": "No",
      "messages": [],
      "evaluationStatus": "SUCCEEDED"
    }
  ]
}

Designing a decision service using DMN models

Decision-authoring assets in IBM Business Automation Manager Open Editions

IBM Business Automation Manager Open Editions BPMN and DMN modelers

Installing the IBM Business Automation Manager Open Editions VS Code extension bundle

Configuring the IBM Business Automation Manager Open Editions standalone editors

Creating and executing DMN and BPMN models using Maven

Decision Model and Notation (DMN)

DMN conformance levels

DMN decision requirements diagram (DRD) components

Rule expressions in FEEL

Data types in FEEL

Built-in functions in FEEL

Conversion functions

Boolean functions

String functions

List functions

Loop statements

Numeric functions

Date and time functions

Range functions

Temporal functions

Sort functions

Context functions

Variable and function names in FEEL

DMN decision logic in boxed expressions

DMN decision tables

Hit policies in DMN decision tables

Boxed literal expressions

Boxed context expressions

Boxed relation expressions

Boxed function expressions

Boxed invocation expressions

Boxed list expressions

DMN model example

DMN support in IBM Business Automation Manager Open Editions

Configurable DMN properties in IBM Business Automation Manager Open Editions

Configurable DMN validation in IBM Business Automation Manager Open Editions

Creating and editing DMN models in Business Central

Defining DMN decision logic in boxed expressions in Business Central

Creating custom data types for DMN boxed expressions in Business Central

Included models in DMN files in Business Central

Including other DMN models within a DMN file in Business Central

Including PMML models within a DMN file in Business Central

Creating DMN models with multiple diagrams in Business Central

DMN model documentation in Business Central

DMN designer navigation and properties in Business Central

DMN model execution

Embedding a DMN call directly in a Java application

Executing a DMN service using the KIE Server Java client API

Executing a DMN service using the KIE Server REST API

REST endpoints for specific DMN models

Additional resources