<ul data-eligibleForWebStory="false"><li>Next activity prediction in business processes is crucial for optimizing service-oriented architectures like microservices environments and distributed enterprise systems.</li><li>Existing sequence-based methods struggle to capture non-sequential relationships arising from parallel executions and conditional dependencies, while graph-based approaches lack adaptability due to homogeneous representations and static structures.</li><li>A novel framework called RLHGNN is introduced to transform event logs into heterogeneous process graphs, offering flexible graph structures tailored to individual process complexities through reinforcement learning and heterogeneous graph convolution.</li><li>RLHGNN has demonstrated superior performance over existing methods in predicting next activities, with minimal latency, making it a practical solution for real-time business process monitoring applications.</li></ul>

RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes

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