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Learning meaningful representations of data is an important aspect of machinelearning and has recently been successfully applied to many domains likelanguage understanding or computer vision. Instead of training a model for onespecific task, representation learning is about training a model to capture alluseful information in the underlying data and make it accessible for apredictor. For predictive process analytics, it is essential to have allexplanatory characteristics of a process instance available when makingpredictions about the future, as well as for clustering and anomaly detection.Due to the large variety of perspectives and types within business processdata, generating a good representation is a challenging task. In this paper, wepropose a novel approach for representation learning of business processinstances which can process and combine most perspectives in an event log. Inconjunction with a self-supervised pre-training method, we show thecapabilities of the approach through a visualization of the representationspace and case retrieval. Furthermore, the pre-trained model is fine-tuned tomultiple process prediction tasks and demonstrates its effectiveness incomparison with existing approaches.

Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks