In this article, we will describe some of the recent updates to the DMN Validation module (kie-dmn-validation) and how the migration to make use of the Executable Model enabled a number of use-cases, such as porting the functionality on the Kogito platform. Introduction The Drools DMN Engine provides static and semantic validation of DMN models:Read more →
This post summarises the current design of REST endpoints which are automatically generated and made available, when using DMN model assets in a Kogito application. This work was finalised as part of KOGITO-2840. Introduction For each DMN model in a Kogito application, a collection of REST endpoints is automatically code generated based on the contentRead more →
presentation byBruce Silver, MethodAndStyle.comand Matteo Mortari, Red Hat At this year’s Decision Camp 2020, I had the opportunity to present with Bruce Silver a complete overview of the DMN Validation features, and some of the research areas we are working together on: I hope this can be an helpful summary of what you should look at whenRead more →
In this post, I would like to share interesting insights about recursion support in DMN and highlights how specific properties of the FEEL language enable functional programming constructs to be modeled in DMN. We are going to start from a basic example, in order to demonstrate how the Business Friendliness nature of the FEEL languageRead more →
We are always looking to improve the performance of the Drools DMN open source engine. We have recently reviewed a DMN use-case where the actual input population of Input Data nodes varied to some degree; this highlighted a suboptimal behavior of the engine, which we improved in recent releases. I would like to share ourRead more →
