Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning?

TL;DR

This paper evaluates the impact of inter-case features and quantum machine learning models on predictive process monitoring accuracy, demonstrating notable improvements and discussing practical challenges with early quantum hardware.

Contribution

It benchmarks the effect of inter-case features on prediction accuracy and explores the potential of quantum machine learning models in process monitoring.

Findings

Inter-case features improve accuracy by over 4%.

Quantum models are competitive in certain configurations.

Quantum hardware limitations affect runtime and overfitting risk.

Abstract

Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in machine learning research, several methods have been proposed to predict the next activity, outcome or remaining time of a process automatically. Still, building a model with high predictive power requires both - intrinsic knowledge of how to extract meaningful features from the event log data and a model that captures complex patterns in data. This work builds upon the recent progress in inter-case Predictive Process Monitoring (PPM) and comprehensively benchmarks the impact of inter-case features on prediction accuracy. Moreover, it includes quantum machine learning models, which are expected to provide an advantage over classical models with a scaling amount of feature dimensions. The evaluation on real-world training data from the BPI challenge shows that the inter-case features provide a significant boost by more than four percent in accuracy and quantum algorithms are indeed competitive in a handful of feature configurations. Yet, as quantum hardware is still in its early stages of development, this paper critically discusses these findings in the light of runtime, noise and the risk to overfit on the training data. Finally, the implementation of an open-source plugin demonstrates the technical feasibility to connect a state-of-the-art workflow engine such as Camunda to an IBM quantum computing cloud service.