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Business Process Representation Learning

Peter Pfeiffer
In: Christian Janiesch; Chiara Di Francescomarino; Thomas Grisold; Akhil Kumar; Jan Mendling; Brian Pentland; Hajo Reijers; Mathias Weske; Robert Winter (Hrsg.). BPM 2022 Best Dissertation Award, Doctoral Consortium, and Demonstration & Resources Track. Business Process Management (BPM-2022), Münster, Germany, CEUR, 9/2022.


Data stored in information systems gathered from the execution of business processes is a rich source of information. Process mining aims to extract and gain knowledge from such data, usually captured in event logs, in order to understand and improve the business processes. From a data-science perspective, event log data is a very interesting yet complex data modality. It does not only describe the process from the control-flow perspective, but also contains additional information like entities and organizations involved, temporal aspects and much more. While there is a lot of work on applying existing machine learning techniques on event log data for solving a specific problem, little work has focused on how to learn from such data effectively. This work presents the idea of developing representation learning models for event logs, i.e. neural-network-based methods specifically designed for this data modality, which learn generic and rich representations of events and cases. The representations are expected to be used for solving different business problems such as process prediction, anomaly detection or other process mining tasks more efficiently and effectively.

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