Automatically discovering a process model from an event log is the prime problem in process mining. This task is so far approached as an unsupervised learning problem through graph synthesis algorithms. Algorithmic design decisions and heuristics allow for efficiently finding models in a reduced search space. However, design decisions and heuristics are derived from assumptions about how a given behavioral description - an event log - translates into a process model and were not learned from actual models which introduce biases in the solutions. In this paper, we explore the problem of supervised learning of a process discovery technique D. We introduce a technique for training an ML-based model D using graph convolutional neural networks; D translates a given input event log into a sound Petri net. We show that training D on synthetically generated pairs of input logs and output models allows D to translate previously unseen synthetic and several real-life event logs into sound, arbitrarily structured models of comparable accuracy and simplicity as existing state of the art techniques for discovering imperative process models. We analyze the limitations of the proposed technique and outline alleys for future work.

中文翻译：

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使用图神经网络的过程发现

从事件日志中自动发现流程模型是流程挖掘的主要问题。到目前为止，这项任务是通过图合成算法作为无监督学习问题来处理的。算法设计决策和启发式方法允许在减少的搜索空间中有效地找到模型。然而，设计决策和启发式是从给定的行为描述——事件日志——如何转化为过程模型的假设中推导出来的，而不是从在解决方案中引入偏差的实际模型中学习的。在本文中，我们探讨了过程发现技术 D 的监督学习问题。我们介绍了一种使用图卷积神经网络训练基于机器学习的模型 D 的技术；D 将给定的输入事件日志转换为健全的 Petri 网。我们表明，在合成生成的输入日志和输出模型对上训练 D 允许 D 将以前看不见的合成日志和几个现实生活中的事件日志转换为声音、任意结构的模型，其准确性和简单性与现有的最先进技术相媲美，用于发现命令过程模型。我们分析了所提出技术的局限性，并为未来的工作勾勒出小巷。