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

TL;DR

This paper systematically reviews and compares 16 remaining time prediction methods in business process monitoring across diverse real-world datasets, highlighting their relative strengths and weaknesses.

Contribution

It provides a comprehensive taxonomy and cross-benchmark analysis of existing methods, clarifying their comparative performance and experimental setups.

Findings

Different methods perform variably across datasets

Benchmarking reveals strengths and weaknesses of each approach

Standardized evaluation helps guide future research

Abstract

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.