Journal article

Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring

I Verenich, Marlon Dumas, M La Rosa, Fabrizio Maggi, Irene Teinemaa

ACM Transactions on Intelligent Systems and Technology | Association for Computing Machinery | Published : 2019

Abstract

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances of a process, including predictions of the remaining cycle time of running cases of a process. A number of approaches to tackle this latter prediction problem have been proposed in the literature. However, due to differences in the experimental setups, choice of datasets, evaluation measures and baselines, the relative performance of various methods remains unclear. This article presents a systematic review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 methods based ..

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Grants

Awarded by Australian Research Council


Awarded by Estonian Research Council


Funding Acknowledgements

This research is partly funded by the Australian Research Council (grant DP180102839) and the Estonian Research Council (grant IUT20-55). Computational resources and services used in this work were provided by the HPC and Research Support Group at Queensland University of Technology