The recomminder: A decision support tool for predictive business process monitoring

Drodt C, Weinzierl S, Matzner M, Delfmann P (2021)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Pages Range: 131-135

Conference Proceedings Title: Proceedings of the BPM 2021 Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium

Event location: Rom IT

Abstract

Predictive business process monitoring (PBPM) provides a set of techniques to optimize the performance of operational business processes. Most recent PBPM techniques learn predictive models from historical event log data using machine learning algorithms (ML). However, there is no silver bullet approach for different event logs, and their performance depends on the characteristics of the underlying event logs. This paper demonstrates the decision support tool Recomminder. The main idea of our tool is to recommend an appropriate pre-processing procedure, an ML algorithm, and the hyper-parameter configuration for a new event log based on its characteristics. While our tool can support researchers to better understand the relation between event log characteristics and ML-driven PBPM techniques, it supports practitioners in developing effective PBPM techniques.

Authors with CRIS profile

Sven Weinzierl Lehrstuhl für Digital Industrial Service Systems Martin Matzner Lehrstuhl für Digital Industrial Service Systems

How to cite

APA:

Drodt, C., Weinzierl, S., Matzner, M., & Delfmann, P. (2021). The recomminder: A decision support tool for predictive business process monitoring. In Proceedings of the BPM 2021 Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium (pp. 131-135). Rom, IT.

MLA:

Drodt, Christoph, et al. "The recomminder: A decision support tool for predictive business process monitoring." Proceedings of the International Conference on Business Process Management, Rom 2021. 131-135.

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