Transfer learning for predictive process monitoring

Ließmann A, Wang W, Weinzierl S, Zilker S, Matzner M (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Conference Proceedings Title: Proceedings of the 32nd European Conference on Information Systems

Event location: Paphos, Cyprus

Abstract

Event log data reflects the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms this data into value; it creates process-related predictions to provide insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts of event data or other relevant resources that might not be readily available, preventing some organizations from taking advantage of PPM. In this paper, we present a transfer-learning-based technique for PM, allowing organizations without suitable data or other relevant resources to implement PPM for effective decision support. We instantiate our artifact, and apply and evaluate it on a real-life use case. The use case includes two event logs for purchase-to-pay processes of two organizations. Our results provide evidence that knowledge of a process of one organization can be transferred to a similar process of another organization to enable PPM in the target organization.

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Related research project(s)

Involved external institutions

Technische Hochschule Nürnberg "Georg Simon Ohm" Germany (DE)

How to cite

APA:

Ließmann, A., Wang, W., Weinzierl, S., Zilker, S., & Matzner, M. (2024). Transfer learning for predictive process monitoring. In Proceedings of the 32nd European Conference on Information Systems. Paphos, Cyprus.

MLA:

Ließmann, Annina, et al. "Transfer learning for predictive process monitoring." Proceedings of the European Conference on Information Systems, Paphos, Cyprus 2024.

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