Goal-driven Variant Categorization

目标驱动的变体分类

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.

摘要： 流程挖掘很少能直接产生单一且连贯的流程结构。在分析过程中，一个常见的步骤是根据结构相似性对流程变体进行聚类，然后为生成的组分配业务含义。由于这些分区并非源自组织的目标，分析师必须手动解读并将变体整合为具有业务意义的类别。随着变体数量和复杂性的增加，这一依赖主观判断的步骤变得愈发困难。

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 transformed into a textual narrative describing its behavior, and a Large Language Model (LLM) interprets it in the context of the goal model and assigns the variant to the most appropriate category. LLM-based semantic reasoning connects low-level process behavior with analyst-defined business goals.

在本文中，我们提出了一种目标驱动的变体分类方法，旨在逆转这一工作流程。我们首先构建一个预定义分类轴的组织目标模型。每个变体都被转换为描述其行为的文本叙述，随后由大语言模型（LLM）在目标模型的语境下进行解读，并将变体分配到最合适的类别中。基于 LLM 的语义推理将底层的流程行为与分析师定义的目标联系了起来。

We instantiate this approach end-to-end and evaluate it on three public logs differing substantially in scale and behavioral diversity. Goal-model guidance yields partitions that differ from those produced by unguided induction and respond to controlled edits to the declared alternatives, at the cost of authoring a goal model.

我们对该方法进行了端到端的实例化，并在三个在规模和行为多样性上存在显著差异的公共日志上进行了评估。实验结果表明，目标模型的引导所产生的分区与无引导归纳法产生的结果不同，并且能够响应对既定方案的受控编辑，其代价是需要预先构建目标模型。

Paper Details:

Authors: Daniel Calegari, Daniel Amyot
Date: 18 Sep 2026
Subjects: Artificial Intelligence (cs.AI); Databases (cs.DB); Software Engineering (cs.SE)
DOI: 10.48550/arXiv.2609.22475

论文详情：

作者： Daniel Calegari, Daniel Amyot
日期： 2026年9月18日
学科： 人工智能 (cs.AI)；数据库 (cs.DB)；软件工程 (cs.SE)
DOI： 10.48550/arXiv.2609.22475