DMN Execution Engine
The Decision Model & Notation Standard (DMN) is a powerful means of representing the business logic of operational decisions – from targeted pricing and recommendation of products to determining which customers get automatic approval or to satisfying regulatory compliance – in a transparent and accountable way.
RapidGen Software offers users of DMN modeling tools a powerful execution engine, able to handle complex logic at scale and execute orders of magnitude faster than comparable products.
While adoption of DMN as a valuable tool for business analysts and stakeholders has been increasing since the standard became official in 2015, enterprise class execution of DMN models and integration of analytic and AI components are now the priorities.
“with BPMN, only a small fraction of modeling projects are intended for automation in an engine, but with decision models, that fraction is closer to 100%.”
Bruce Silver, Method & Style Blog
RapidGen’s depth of experience in decision logic programming in large organizations has led to their creation of an optimized execution engine able to convert DMN from any modeling tool into machine code that can be executed on Linux and PC platforms.
Product Features / Benefits:
- Accepts XML output from any DMN Modeling Tool which is conformant with the DMN Standard 1.3
- High speed execution – extremely fast, efficient, fully-compiled code
- Scalable
- Handles complex logic
- Provides Decision Intelligence through Integration with AI and analytic components
- Provides Decision Insight by recording the rationale for all decisions made
The decision model (Fig 1, below) is an example of a readable, transparent and accountable description of business logic using the DMN Standard. This standard is designed to allow business subject matter experts and stakeholders to control the evolution of operational decisions. Genius can ingest this model and compile it into executable, highly efficient machine code that can process 100k+ decisions per second on modest hardware whilst retaining full traceability for all outcomes.
Figure 1