Users can use OpenRules Decision Manager to create, install, and run operational decision services as shown in the steps

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Steps 1 and 2 include creating a plain English problem description, a business glossary, and test cases with sample inputs and expected results. After a series of edits, you should have a good understanding of the business problem and move on to step 3.

2. In phases two and three, you will populate your decision model with rules, decision tables, and programming elements. These components, along with a glossary and test cases, make up your decision model. To build business logic in Excel decision tables scattered across many folders, you can use MS Excel or Google Sheets as your table editor. Adding additional decision logic (for example, a new decision table) is an iterative process that requires validating your model against a set of test cases to ensure that it works correctly. The OpenRules Decision Model Explorer, a graphical decision modeling environment, makes it easy to create, test, and debug decision models.

Business analysts typically write business logic in Excel spreadsheets and then run it through the standard rules engine. If you need to automatically teach specific business rules using historical data, you can use Rule Learner’s machine learning components in your decision model.

For example, the Rule Solver can be useful if your model opportunity seekers mailing list has optimization subproblems to address. The Rule Learner and Rule Solver of the Rule Engine are also fully integrated with the Decision Manager.

Thus, OpenRules decision models can include logic based on expert knowledge (Business Rules) and historical data (Optimization). Therefore, the decision model can use widely available rule engines, learners, constraints, or linear solvers. Business analysts can complete steps 1, 2, and 3 on their own or collaborate with software developers to build some components of the decision model using the Rules Learner and Rule Solver Java APIs.

3. After testing the business decisions, you can easily integrate them into any Java application. Moreover, they can run on any server such as Apache Tomcat or IBM WebSphere. Moreover, end users can deploy them in the cloud as a decision microservice using any serverless architecture provided by major cloud providers such as Amazon, Google, and Microsoft or IBM through a simple Java API.