SOURCE-LINKED INTELLIGENCE
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 firs
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T18:35:41.000Z
First collected: 2026-09-23T12:01:45.602Z. This is not the publication date.