Impact of Inter-Case Features on Structure-Aware Next Activity Prediction
Year: 2024
Type: conference-paper
Abstract: Predictive process monitoring (PPM) techniques analyze event logs from enterprise information systems to predict the outcomes of ongoing processes. Traditional PPM approaches model process executions using intra-case features, which are based on information specific to the execution itself. However, inter-case features, which capture the broader context of the environment in which a process is executed, should also be considered. In this paper, we define new inter-case features capturing system workload in terms of concurrent cases and resources utilization. We test their impact using a Graph Neural Network architecture, which accounts for parallel activity executions. Results show that integrating inter-case features substantially improves classification performance across various real-world event logs.
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4410427754