Probabilistic learning of processing and waiting times: a scalable approach for process event log data

Key Points

• Key findings reveal that probabilistic learning effectively models processing and waiting times throughout various processes, indicating its practical relevance.
• The method employs event log data, analyzing time dynamics to enhance understanding of process efficiency, particularly in complex systems.
• Observational analysis applied to diverse datasets shows significant insights into time-related metrics that influence operational outcomes.
• The findings highlight the potential for scalability in analyzing extensive datasets, paving the way for optimized process management and efficiency.