Goal-driven Variant Categorization
Process discovery rarely yields a single coherent process structure. For analysis, a common step is to cluster process variants based on structural similarity and then assign business meaning to the resulting groups. Since these partitions are not derived from the organization's goals, analysts must manually interpret and consolidate variants into business-meaningful categories. This judgment-intensive step becomes increasingly difficult as the number and complexity of variants grow. In this paper, we propose a goal-driven approach to variant categorization that reverses this workflow. We first author an organization's goal model that predefines the categorization axis. Each variant is tr...
| Authors | Daniel Calegari, Daniel Amyot |
|---|---|
| Primary category | cs.AI |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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