In this blog post I want to share the results of a technical exploration in bridging, bringing together and integrating a diverse set of technologies and platforms, ranging from classic spreadsheet applications (Excel) to serverless platforms (Knative on OpenShift) to technical rules executed by our rule engine Drools! Introduction This content has been inspired byRead more →
We put Drools inside Kubernetes’ control plane …you wouldn’t believe what happened next! 😉 This is a longer post on the current status of explorations with RuleOps; you may want to skim or skip to different sections, depending on your interests. RuleOps in a nutshell Read time: ~ 1 minuteKubernetes is an orchestration platform, veryRead more →
This blog post is a quick update on a demo in labeling CNCF’s CloudEvents. Introduction Categorizing events is a general, common use-case; in the context of this post, we will delve into labeling CNCF’s CloudEvents for Intelligent Response Management (IRM) which can find application in several ways. One way is to categorize and prioritize differentRead more →
Managing exceptional rules is easy, thanks to Drools and Kogito! In this use-case, we have a base business process and a default knowledge base with rules, which can be overridden by specific entities or departments as needed. We actually have several architectural options we could implement! The simplest architectural option I can think about, drawsRead more →
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 →
