Process Discovery for Event Logs with Multi-Occurrence Event Types

One of the most actively researched areas in the field of process mining is process discovery, which aims to construct a schema that aligns with existing event trace sequences. Current standard industrial workflow schema induction methods impose certain limitations on the system being examined. To address the shortcomings, this article proposes a novel solution that employs graph neural networks and convolutional neural networks to perform schema discovery. In the first phase of schema generation, we perform equivalence prediction, implemented as an edge prediction task. From the obtained equivalence network, we identify the target schema nodes, which correspond to the maximal quasi-cliques of this network. The results of the performed efficiency tests demonstrate that the proposed method can manage such complex cases that are not covered by standard process discovery methods, and it provides more compact and more precise schema graphs.