DecisionCAMP 2019: Serverless DROOLS and the Digital Engineer
Blog: Column 2 - Sandy Kemsley
How and Why I Turned a Rule Engine into a First-Class Serverless Component. Mario Fusco and Matteo Mortari, Red Hat
Mario Fusco, who heads up the Drools project within Red Hat, presented on modernization of the Drools architecture to support serverless execution, using GraalVM and Quarkus. He discussed Kogito, a cloud-native, open source business automation project that uses Red Hat process and decision management along with Quarkus.
I’m not a JAVA developer and likely did not appreciate many of the details in the presentation, hence the short post. You can check out his slides here.
Combining DMN, First Order Logic and Machine Learning: The creation of Saint-Gobain Seals’ Digital Engineer. Nicholas Decleyre, Saint-Gobain and Bram Aerts, KU Leuven
The seals design and manufacturing unit of Saint-Gobain had the goal to create a “digital engineer” to capture knowledge, with the intent to standardize global production processes, reduce costs and time to market, and aid in training new engineers. They create an engineering automation tool to automatically generate solutions for standard designs, and an engineering support tool to provide information and other support to engineers while they are working on a solutions.
Automation for known solutions is fairly straightforward in execution: given the input specifications, determine a standard seal that can be used as a solution. This required quite a bit of knowledge elicitation from design engineers and management, which could then be represented in decision tables and FEEL for readability by the domain experts. Not only the solution selection is automated, however: the system also generates a bill of materials and pricing details.
The engineering support system is for when the solution is not known: a design engineer uses the support system to experiment on possible solutions and compare designs. This required building a knowledge base in first-order logic to define physical constraints and preferences, represented in IDP, then allowing the system to make recommendations about a partial or complete solution or set of solutions. They built a standalone tool for engineers to use this system, presenting a set of design constraints for the engineer to apply to narrow down the possible solutions. They compared the merits of DMN versus IDP representations, where DMN is easier to model and understand, but has limitations in what it can represent as well as being more cumbersome to maintain. At RuleML yesterday, they presented a proposal for extended DMN for better representing constraints.
They finished up talking about potential applications of machine learning on the design database: searching for “similar” existing solutions, learning new constraints, and checking data consistency. They have several automated engineering tools in development, with one in testing and one in production. Their engineering support tool has working core functionality although need to expand the knowledge base and prototype the UI. On the ML work, they are expecting to have a prototype by the end of this year.
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