  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.
