Towards reliable predictive process monitoring

Abstract

Predictive process monitoring is concerned with anticipating the future behavior of running process instances. Prior work primarily focused on the performance of monitoring approaches and spent little effort on understanding other aspects such as reliability. This limits the potential to reuse the approaches across scenarios. From this starting point, we discuss how synthetic data can facilitate a better understanding of approaches and then use synthetic data in two experiments. We focus on prediction as classification of process instances during execution, solely considering the discrete event behavior. First, we compare different feature representations and reveal that sub-trace occurrence can cover a broader variety of relationships in the data than other representations. Second, we present evidence that the popular strategy of cutting traces to certain prefix lengths to learn prediction models for ongoing instances is prone to yield unreliable models and that the underlying problem can be avoided by using approaches that learn from complete traces. Our experiments provide a basis for future research and highlight that an evaluation solely targeting performance incurs the risk of incorrectly assessing benefits and limitations.