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Predictive process monitoring is an evolving research field that studies howto train and use predictive models for operational decision-making. One of theproblems studied in this field is that of predicting the sequence of upcomingactivities in a case up to its completion, a.k.a. the case suffix. Theprediction of case suffixes provides input to estimate short-term workloads andexecution times under different resource schedules. Existing methods to addressthis problem often generate suffixes wherein some activities are repeated manytimes, whereas this pattern is not observed in the data. Closer examinationshows that this shortcoming stems from the approach used to sample thesuccessive activity instances to generate a case suffix. Accordingly, the paperintroduces a sampling approach aimed at reducing repetitions of activities inthe predicted case suffixes. The approach, namely Daemon action, strikes abalance between exploration and exploitation when generating the successiveactivity instances. We enhance a deep learning approach for case suffixpredictions using this sampling approach, and experimentally show that theenhanced approach outperforms the unenhanced ones with respect to control-flowaccuracy measures.

Enhancing the Accuracy of Predictors of Activity Sequences of Business Processes