🤖 AI Summary
Business process optimization remains challenging due to fragmented methodologies across process mining, predictive process monitoring, and process-aware recommendation—each operating in isolation without a unified theoretical foundation or integration framework.
Method: This paper proposes a closed-loop optimization framework that systematically integrates Alpha algorithm/Inductive Miner for process discovery, LSTM/Transformer for runtime prediction, collaborative filtering/graph neural networks for action recommendation, and explainable AI (XAI) for interpretability—enabling automated bottleneck identification, anomaly forecasting, and prescriptive optimization from event logs.
Contribution/Results: We establish the first unified conceptual boundary, evolutionary taxonomy, and synergy paradigm across the three domains; construct a comprehensive classification schema covering 120+ studies; clarify application scopes and standardized evaluation benchmarks; and deliver an industrially actionable methodology selection guide with validated deployment pathways.
📝 Abstract
Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization.