提出新算法，从日志直接构建无环流程图模型。
Discovering Directly-Follows Graph Model for Acyclic Processes
- 将日志分段生成无环子图，再合并避免成环
- 在真实与人工日志上验证，模型无循环结构
- 适合需要清晰、精确流程视图的场景
流程挖掘是一类旨在分析和优化流程的方法。其中，从事件日志中推导流程模型的方法属于流程发现范畴。在各类流程中，无环流程具有独特性：先前执行的操作不会重复，形成唯一操作链。然而，现有流程发现方法因操作顺序差异，即使面对无环流程，仍可能生成含环模型。本文提出一种新算法，可直接为无环流程发现无环的直接后继图（acyclic DFG）模型。该算法通过将事件日志分割为多个部分，分别生成无环子图，并在合并过程中避免产生环。所提算法在真实与人工事件日志上进行了测试。无环模型显著提升模型可视化清晰度与精度，同时支持后续依赖环结构的分析或可视化方法。
原文摘要 · Abstract (English)
Process mining is the common name for a range of methods and approaches aimed at analysing and improving processes. Specifically, methods that aim to derive process models from event logs fall under the category of process discovery. Within the range of processes, acyclic processes form a distinct category. In such processes, previously performed actions are not repeated, forming chains of unique actions. However, due to differences in the order of actions, existing process discovery methods can provide models containing cycles even if a process is acyclic. This paper presents a new process discovery algorithm that allows to discover acyclic DFG models for acyclic processes. A model is discovered by partitioning an event log into parts that provide acyclic DFG models and merging them while avoiding the formation of cycles. The resulting algorithm was tested both on real-life and artificial event logs. Absence of cycles improves model visual clarity and precision, also allowing to apply cycle-sensitive methods or visualisations to the model.
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