Goal-driven Variant Categorization
Quick summary
arXiv:2609.22475v1 Announce Type: new Abstract: 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 cat
Key takeaways
- arXiv:2609.22475v1 Announce Type: new Abstract: 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.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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