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Feature extraction from event logs for predictive monitoring of business processes

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  • Borchert, Florian

Abstract

In this master’s thesis, we investigate feature extraction techniques for event log traces. We provide an overview over the field of predictive monitoring and analyze existing trace profiles proposed in the literature. Based on this, we apply the results obtained in recent research on process discovery to find meaningful abstractions over related subsequences of events. We assess different feature sets by evaluating their predictive power in a supervised classification setting as well as a semi-supervised outlier detection setting. For this purpose, we use two datasets from public administration, describing complex processes in EU agricultural subsidy management. Our particular goal in this domain is to predict negative outcomes, for instance, additional work due to legal claims or corrections necessary after initial payment decisions.

Suggested Citation

  • Borchert, Florian, 2017. "Feature extraction from event logs for predictive monitoring of business processes," Thesis Commons y6px4, Center for Open Science.
    • Handle: RePEc:osf:thesis:y6px4
    DOI: 10.31219/osf.io/y6px4
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