DARWIN: An online deep learning approach to handle concept drifts in predictive process monitoring

Predictive process monitoring (PPM) is a specific task under the umbrella of Process Mining that aims to predict several factors of a business process (e.g., next activity prediction) based on the knowledge learned from historical event logs. Despite recent PPM algorithms have gained predictive accuracy using deep learning, they commonly perform an offline analysis of event data assuming that logged processes remain in a steady state over time. However, this is often not the real-world case due to concept drifts. The main goal of this work is to solve the next-activity prediction problem under dynamic conditions of business data streams. To this aim, we propose DARWIN as a novel PPM method that detects concept drifts and adapts a deep neural model to concept drifts. A deep empirical analysis of different factors that may influence the performance of DARWIN in streaming scenarios is provided. Experiments with various benchmark event streams show the effectiveness of the proposed approach.

Automated summary

Predictive process monitoring (PPM) is a task in Process Mining that aims to predict factors of a business process based on historical event logs. However, existing PPM algorithms assume steady-state processes, which is not the case in the real world due to concept drifts. This work proposes DARWIN, a PPM method that detects and adapts to concept drifts in business data streams, and provides empirical analysis and experiments to showcase its effectiveness.