目标驱动的变体分类

中文摘要 AI 辅助

流程发现很少能产生单一连贯的流程结构。为了进行分析，一个常见步骤是基于结构相似性对流程变体进行聚类，然后为生成的组赋予业务含义。由于这些划分并非源自组织的目标，分析人员必须手动解释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长，这一判断密集型步骤变得越来越困难。在本文中，我们提出了一种目标驱动的变体分类方法，它逆转了这一工作流程。我们首先编写组织的目标模型，该模型预定义了分类轴。每个变体被转换为描述其行为的文本叙述，大型语言模型（LLM）在目标模型的上下文中对其进行解释，并将该变体分配到最合适的类别。基于LLM的语义推理将低层流程行为与分析人员定义的业务目标联系起来。我们端到端地实例化了这种方法，并在三个在规模和行为多样性上差异显著的公开日志上进行了评估。目标模型的指导所产生的划分不同于无指导归纳所产生的划分，并且对声明备选项的受控编辑做出响应，其代价是需要编写目标模型。

英文摘要

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 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. 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.

发表机构

• Universidad ORT Uruguay（乌拉圭ORT大学）
• University of Ottawa（渥太华大学）

机构由 AI 辅助整理，请以论文原文为准。