Following up from a previous blog post about integrating Drools with the Open Prediction Service, in this new post we want to share the current results from another exploration work: this time integrating Drools with research on Transparent Machine Learning by IBM. Introduction Transparency is a key requirement in many business sectors, from FSI (FinancialRead more →
We’re introducing an (experimental) DMN FEEL handbook, an helpful companion for your DMN modeling activities! You can access this new helpful resource at the following URL: https://kiegroup.github.io/dmn-feel-handbook. Key features include: FEEL built-in functions organised by category tested and integrated FEEL examples Responsive design: easily access on Mobile, Tablet and Desktop from your favourite browser! …andRead more →
This short tutorial walks you through the configuration and deployment of a simple Drools serverless application, including autoscaling with scale to zero, thanks to Kogito, Quarkus, OpenShift Serverless with Kubernetes and Knative! Step 1: Drools app creation with code.quarkus.io To generate the application as shown in the video, you can use this link: https://code.quarkus.io/?e=org.kie.kogito%3Akogito-quarkus-decisions&e=resteasy-jackson&e=kubernetes&e=container-image-jib TheRead more →
In this blog post we’re going to explore an integration between the Drools DMN Engine and another open source project from IBM: "Open Prediction Service" (OPS). Introduction Integrating symbolic AIs (rule engines, KRR, etc) with Machine Learning predictive models is an effective strategy to achieve pragmatical, and often more eXplainable, AI solutions. We have alsoRead more →
In this short update, I want to share with you about an experimental feature to leverage the extensibility of the DMN specification to evaluate expressions using a plurality of expression languages, such as JavaScript, Power Fx, and potentially many more! For the running example in this post, let’s use the Body Mass Index (BMI) calculationRead more →
This is a second iteration of a previous post, where we implemented EIP patterns using just Drools and Apache Camel. In this post instead, I want to share with you how to implement a complete, end-to-end Content Based Routing solution using Quarkus as a developer platform, including: Drools DMN Engine, Kogito, Apache Camel, AtlasMap andRead more →
In this post I want to share an interesting use case of data enrichment, using DMN with BPMN and other open standards. The typical usage pattern for data enrichment is the following: a complex data structure containing several attributes is provided as input; based on some computations and decision results, the original structure is enrichedRead more →
an experimental JBang catalog to quickly operate some KIE capabilities, such as DMN and FEEL evaluation on the Command Line!
I am very glad for the opportunity to have presented at IIBA this session on DMN patterns with Denis Gagné CEO & CTO of Trisotech! how business analysts can use the DMN open-standard to capture the requirements for operational business decisions some of the recurring basic patterns in modeling (Q&A, Scoring, Classification and Categorisation, Ranking..)Read more →
In this post I want to highlight all the integrations of the kie-dmn-validation module on several platforms. What is it? In a previous post, we have seen how the Drools DMN validation module was integrated specifically on the Kogito platform. As a short review, the kie-dmn-validation module offer several features for: validation of DMN modelRead more →
