计算机科学
启发式
过程采矿
机器学习
人工智能
图形
事件(粒子物理)
业务流程发现
Petri网
过程(计算)
监督学习
卷积神经网络
人工神经网络
数据挖掘
理论计算机科学
在制品
算法
业务流程建模
营销
业务
业务流程
物理
量子力学
操作系统
作者
Dominique Sommers,Vlado Menkovski,Dirk Fahland
标识
DOI:10.1016/j.is.2023.102209
摘要
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. We introduce a technique for training an ML-based model using graph convolutional neural networks, which translates a given input event log into a sound Petri net. We show that training this model on synthetically generated pairs of input logs and output models allows it 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 in imperative mining. We analyze the limitations of the proposed technique and outline alleys for future work.
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