Application of Neural Network Tools in Process Mining

Abstract

Dominant current technologies in process mining use schema induction approaches based on graph and au- tomaton methods. The paper investigates the application of neural network approaches in schema induction focusing on three alternative architectures: MLP, CNN and LSTM networks. The proposed neural network models can be used to discover XOR, loop and parallel execution templates. In the case of loop detection, the performed test analyses show the dominance of CNN approach where the string is represented with a two- dimensional similarity matrix. The usability of the proposed approach is demonstrated with test examples.