Decision Modeling in SMARTS: DMN 1.3
9 AM PT / 12 PM ET
The DMN standard (Decision Model and Notation) has brought to decision management a notation that comes with a powerful underlying methodology. With DMN, business analysts think about the ultimate decision(s) in a structured way, starting from the the top-level decision into smaller sub-decisions. This iterative process is very friendly, and very easy to share with colleagues.
Since its inception, Sparkling Logic has fully supported the Decision Model and Notation (DMN) Standard.
- In addition to a decision table, a rule set, or a literal expression, the implementation of a decision can consist of a context, a function, an invocation, a list, or a relation. A business knowledge model can now be implemented by a function.
- Additional DMN diagram elements, such as text annotations, groups, or decision services.
- The DMN XML interchange format, including DMNDI, to import decision models from other DMN tools, or export them.
- Multiple expression languages: SparkL (the SMARTS expression language), FEEL (the DMN expression language), free-form, or any other.
- The execution of decision models from SMARTS decisions, using decision model invocation rule sets (exactly like lookup or PMML models are executed from SMARTS).
If your decisioning projects necessitate formal requirements and decision modeling, this webinar is for you.
AboutSparkling Logic is a Silicon Valley company dedicated to helping businesses automate and improve the quality of their operational decisions with a powerful digital decisioning platform, accessible to business analysts and ‘citizen developers’. Sparkling Logic’s customers include global leaders in financial services, insurance, healthcare, retail, utility, and IoT.
Sparkling Logic SMARTSTM (SMARTS for short) is a cloud-based, low-code, AI-powered business decision management platform that unifies authoring, testing, deployment and maintenance of operational decisions. SMARTS combines the highly scalable Rete-NT inference engine, with predictive analytics and machine learning models, and low-code functionality to create intelligent decisioning systems.