Using Machine Learning to Classify Process Model Elements for Process Infrastructure Analysis - Avila, Diego Toralles; Moura, Vitor Camargo de; Thom, Lucineia Heloisa (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2023: Anais do XIX Simpósio Brasileiro de Sistemas de Informação, 2023)
Context: Business Process Management is an increasingly important discipline adopted by organizations to model, analyze, and implement their business processes. The complexity of understanding business processes is reduced when they are analyzed through process models. Problem: While many techniques assist and automate process modeling, the same is different for process analysis, which may reduce the effectiveness and consistency of BPM in improving business processes. Solution: This paper reports on our methodology to train a machine learning algorithm to automatically analyze process model elements (e.g., activities, swimlanes, data objects) and classify them according to three categories of infrastructure information: (i) process participants; (ii) processed documents and information; (iii) systems, technologies and tools. IS Theory: This work was conceived under the aegis of Organizational Learning Theory. The training of machine learning models seeks improvement over previous analysis methods through experimentation. Method: The research described in this paper is prescriptive and quantitative, organized through four phases: (i) model collection; (ii) dataset building; (iii) data evaluation; (iv) machine learning classifier development. Summary of Results: From a collection of 85 process models, three training datasets were created, with an average of 480 process activities each. We obtained accuracies between 88% and 96% depending on the category analyzed. Contributions and Impact in the IS area: The main contribution of this research is the methodology developed to help automate business process analysis through machine learning training datasets and models. We expect this approach to assist in achieving more consistent results in the analysis of large process architectures.