Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs
This addresses a neglected issue in business process management for scenarios with limited data, but it is incremental as it focuses on evaluating existing techniques rather than introducing new ones.
The paper tackles the problem of insufficient data in predictive business process monitoring by developing an evaluation framework to compare existing approaches for small event logs, applying it to state-of-the-art methods.
Predictive business process monitoring is concerned with the prediction how a running process instance will unfold up to its completion at runtime. Most of the proposed approaches rely on a wide number of different machine learning (ML) techniques. In the last years numerous comparative studies, reviews, and benchmarks of such approaches where published and revealed that they can be successfully applied for different prediction targets. ML techniques require a qualitatively and quantitatively sufficient data set. However, there are many situations in business process management (BPM) where only a quantitatively insufficient data set is available. The problem of insufficient data in the context of BPM is still neglected. Hence, none of the comparative studies or benchmarks investigates the performance of predictive business process monitoring techniques in environments with small data sets. In this paper an evaluation framework for comparing existing approaches with regard to their suitability for small data sets is developed and exemplarily applied to state-of-the-art approaches in predictive business process monitoring.