Discovering operational decisions from data-a framework supporting decision discovery from data

Sam Leewis*, Koen Smit, Johan Versendaal

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework's efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
Original languageEnglish
Number of pages20
JournalDecision
DOIs
Publication statusE-pub ahead of print - 24 Oct 2024

Keywords

  • Dmn
  • Decision discovery
  • Decision discovery framework
  • Decision mining
  • Operational decision-making

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