I published an article “Business Decision Models are moving to Serverless World“. In particular, it says:
Two major open-source products, Red Hat Drools and OpenRules, already announced the availability of their new implementations:
- Red Hat is turning Drools into a first-class serverless component and has introduced Kogito that embraces the Quarkus framework and GraalVM’s for super-fast startup times and low memory footprint;
- OpenRules introduced a brand-new product Decision Manager that executes exactly the same decision models as the classical OpenRules BRDMS, but uses a completely new execution mechanism that doesn’t need Excel-based Rules Repositories in run-time anymore as it converts all rules to Java ahead of time. As a result, decision services also can startup almost immediately, be executed within milliseconds, and have a minimal memory footprint. Link
The integrated use of Machine Learning (ML) and Business Rules (BR) is one of the most practical trends in the development of modern decision-making software. OpenRules is involved in this development for more than 10 years starting with our successful ML+BR projects for IRS. Along with a general purpose Rule Learner, we also provide Rule Compressor, that uses ML to compress large decision tables to smaller ones. This recent presentation explains how it works. Continue reading
I was asked by BPM.com to share my thoughts of what to expect in 2019. Digital Decisioning and DMN will continue to play an essential role in BPM. I can see two major trends in this development:
- Simplification. Representation of decision logic within business processes will be de-facto standardized using mainly simple DMN concepts such as decision tables and avoiding complex programming concepts. The simplified approaches such as “Goal-Oriented Decision Modeling” supported by OpenRules will continue to prevail in development of decision models incorporated into real-world business process models.
- Addressing Complex Decision Optimization Problems. So far, human decision modelers were forced to describe exactly HOW to find a decision by handling all possible combinations of business factors using business rules with multiple exceptions on top of exceptions. More powerful decision engines will allow decision modelers to concentrate on WHAT instead of HOW and will automatically determine multiple feasible decisions and select the optimal decision.