Optimized Early Prediction of Business Processes with Hyperdimensional Computing

Abstract

There is a growing interest in the early prediction of outcomes in ongoing business processes. Predictive process monitoring distills knowledge from the sequence of event data generated and stored during the execution of processes and trains models on this knowledge to predict outcomes of ongoing processes. However, most state-of-the-art methods require the training of complex and inefficient machine learning models and hyper-parameter optimization as well as numerous input data to achieve high performance. In this paper, we present a novel approach based on Hyperdimensional Computing (HDC) for predicting the outcome of ongoing processes before their completion. We highlight its simplicity, efficiency, and high performance while utilizing only a subset of the input data, which helps in achieving a lower memory demand and faster and more effective corrective measures. We evaluate our proposed method on four publicly available datasets with a total of 12 binary prediction tasks. Our proposed method achieves an average 6% higher area under the ROC curve (AUC) and up to a 14% higher F1-score, while yielding a 20× earlier prediction than state-of-the-art conventional machine learning- and neural network-based models.

Keywords

• predictive process monitoring
• hyperdimensional computing
• sequence classification
• pattern-based encoding