Semantics and Analysis of DMN Decision Tables

TL;DR

This paper introduces a formal semantics for DMN decision tables, proposes scalable algorithms for analyzing their correctness and completeness, and demonstrates their effectiveness on large real-world datasets.

Contribution

It provides the first formal semantics for DMN tables and scalable algorithms for detecting overlapping and missing rules, enhancing decision table analysis.

Findings

Algorithms successfully detect overlapping rules.

Algorithms identify missing rules in large tables.

Implementation tested on real-world credit data.

Abstract

The Decision Model and Notation (DMN) is a standard notation to capture decision logic in business applications in general and business processes in particular. A central construct in DMN is that of a decision table. The increasing use of DMN decision tables to capture critical business knowledge raises the need to support analysis tasks on these tables such as correctness and completeness checking. This paper provides a formal semantics for DMN tables, a formal definition of key analysis tasks and scalable algorithms to tackle two such tasks, i.e., detection of overlapping rules and of missing rules. The algorithms are based on a geometric interpretation of decision tables that can be used to support other analysis tasks by tapping into geometric algorithms. The algorithms have been implemented in an open-source DMN editor and tested on large decision tables derived from a credit lending dataset.