Hierarchical structuring of bilaterally expanding subtrace patterns for efficient tree-based activity suffix prediction

Abstract Business processes of different domains are potentially prone to high variability, leading to high amounts of uncertainty with respect to future case execution. The field of predictive process monitoring has recognized this fate and developed handling this uncertainty as one of its core concerns for building reliable predictive or prescriptive methods. Over the last decade, deep learning methods have increasingly emerged as a product of this research field and are considered as the preferred approach when it comes to the prediction of next activities or activity suffixes. However, it remains an open question whether deep learning models finally surpass traditional data mining techniques for these tasks. We address this question in our paper by proposing a framework for process event sequence prediction framework which is based on a hierarchical structuring of bilaterally expanding subtraces mined from activity traces which takes their structural relationship and inter-pattern distances into account. The resulting tree structure serves as an efficient alternative approach for currently established deep learning techniques due to its drastically lower model complexity. The hierarchical arrangement can directly be leveraged for forecasting the most probable future activities given the recent trace history. We achieve competitive forecasting results for remaining trace prediction, even surpassing state-of-the-art deep learning approaches on the majority of the analyzed real-world benchmark process event logs while only relying on the available control-flow information.