VaryMinions: Leveraging RNNs to Identify Variants in Event Logs
Sophie Fortz (University of Namur)
Paul Temple (University of Namur)
Xavier DEVROEY (TU Delft - Software Engineering)
Patrick HEYMANS (University of Namur)
GILLES Perrouin (University of Namur)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Business processes have to manage variability in their execution, e.g., to deliver the correct building permit in different municipalities. This variability is visible in event logs, where sequences of events are shared by the core process (building permit authorisation) but may also be specific to each municipality. To rationalise resources (e.g., derive a configurable business process capturing all municipalities' permit variants) or to debug anomalous behaviour, it is mandatory to identify to which variant a given trace belongs. This paper supports this task by training Long Short Term Memory (LSTMs) and Gated Recurrent Units (GRUs) algorithms on two datasets: a configurable municipality and a travel expenses workflow. We demonstrate that variability can be identified accurately (>87%) and discuss the challenges of learning highly entangled variants.