An Overview Of Methods For Acquiring And Generating Decision Models

Decisions are of significant value to organizations. Furthermore, these business decisions are often represented in various knowledge sources, and manually modeling them is costly, tedious, and time-consuming. As decision modeling has seen a surge of interest since the introduction of the Decision Model and Notation (DMN) standard, research interest has also increased regarding automatically extracting decision models. This paper discusses an overview and classification of such techniques, including generating decision models from various knowledge sources such as natural language text, legacy code, other models, or event logs.