Automated decision-making with DMN: from decision trees to decision tables

Abstract

Recent advances in artificial intelligence, especially the subfield of machine learning, is commonly cited as one of the driving forces for digital transformation and innovative business models. Ongoing research is focusing on embedding solutions based on machine learning into business processes which are commonly modelled using the BPMN standard. The Object Management Group has recently adopted the Decision Model and Notation standard. By using the Decision Model and Notation (DMN) it is possible to replace multiple decision points embedded in business processes. The purpose of this research is to provide a method to derive DMN decision tables from the corresponding machine learning model generated by the decision tree classifier. The development is conducted using the Python machine learning library scikit-learn and Camunda Modeler. This approach facilitates and automates the process of converting machine learning models into DMN tables.