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 →
In this post I want to share an interesting use-case of Healthcare message routing, which we implemented using the Drools DMN Engine and Apache Camel, in order to route and dispatch Patient’s Admission-Discharge-Transfer message types to the required Kafka topics and therefore queuing the message to the appropriate sub-system. I believe it is both aRead more →
Modernising kie-server with new and more user-friendly DMN endpoints, better Swagger/OpenAPI documentation, easier JSON-based REST invocations; an intermediate step to help developers transitioning to service-oriented deployments such as a Kogito-based application.
I’ve recorded the following "developer notes" as a support medium to demonstrate the progress and the current integration of several Kogito features. As I believe this could be of interest to a wider audience to have a brief overview of the many capabilities of the Kogito platform for modeling and developing decision services with DMN,Read more →
