Affiliations: Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece
Correspondence:
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Corresponding author: Alexandros Bousdekis, Department of Informatics and Computer Engineering, University of West Attica, Egaleo, Greece. E-mail: albous@uniwa.gr.
Abstract: Process mining is an emerging research field which deals with discovering, monitoring and improving business processes by analyzing and mining data in the form of event logs. Event logs can be extracted by most of the existing enterprise information systems. Predictive business process monitoring is a sub-field of process mining and deals with predictive analytics models on event log data that incorporate Machine Learning (ML) algorithms and deal with various objectives of process instances, such as: next activity, remaining time, costs, and risks. Existing research works on predictions about next activities are scarce. At the same time, Automated Machine Learning (AutoML) has not been investigated in the predictive business process monitoring domain. Therefore, based on its promising results in other domains and type of data, we propose an approach for next activity prediction based on AutoML, and specifically on the Tree-Based Pipeline Optimization Tool (TPOT) method for AutoML. The evaluation results demonstrate that automating the design and optimization of ML pipelines without the need for human intervention, apart from making accessible ML to non-ML experts (in this case, the process owners and the business analysts), also provides higher prediction accuracy comparing to other approaches in the literature.
Keywords: Automated machine learning, process mining, business process, predictive analytics
