Process Discovery Using Graph Neural Networks
This addresses the process mining problem of automatically deriving process models from event logs, offering a supervised alternative to existing unsupervised methods, though it is incremental as it builds on prior techniques.
The paper tackles the problem of supervised process discovery by training a graph convolutional neural network to translate event logs into sound Petri nets, achieving comparable accuracy and simplicity to state-of-the-art techniques on synthetic and real-life logs.
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.