Semantics, Analysis and Simplification of DMN Decision Tables

•Formal semantics of decision tables defined in the Decision Model and Notation (DMN)•General approach to analyze DMN decision tables based on a geometric interpretation thereof•Algorithms for finding overlapping and missing rules in DMN decision tables•Algorithm for simplifying a decision table by...

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Bibliographic Details
Published in:Information systems (Oxford) 2018-11, Vol.78, p.112-125
Main Authors: Calvanese, Diego, Dumas, Marlon, Laurson, Ülari, Maggi, Fabrizio M., Montali, Marco, Teinemaa, Irene
Format: Article
Language:English
Subjects:
Algorithms
Construction standards
Decision analysis
Decision making models
Decision Model
Decision Table
Information systems
Knowledge management
Notation
Semantics
Simplification
Sweep-Line
Tables
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Summary:•Formal semantics of decision tables defined in the Decision Model and Notation (DMN)•General approach to analyze DMN decision tables based on a geometric interpretation thereof•Algorithms for finding overlapping and missing rules in DMN decision tables•Algorithm for simplifying a decision table by means of rule merging The Decision Model and Notation (DMN) is a standard notation to capture decision logic in business applications. 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 and refactoring tasks on these tables. This article puts forward a formal semantics for DMN decision tables and a formal definition of analysis tasks on such tables. The article then proposes a general approach to analyze and refactor decision tables based on a geometric interpretation thereof. This general approach is used to design efficient algorithms for two analysis tasks (detection of overlapping rules and of missing rules) and one refactoring task (simplification of tables via rule merging). The algorithms have been implemented in an open-source DMN editor and tested on large decision tables derived from a credit lending dataset.
ISSN:0306-4379
1873-6076
DOI:10.1016/j.is.2018.01.010