Event Processing

Event processing focuses on efficiently extracting knowledge from continuous data streams, aiming to identify patterns and trigger actions in real-time. Current research emphasizes developing scalable and efficient algorithms, including asynchronous processing techniques, graph neural networks (GNNs), and reinforcement learning approaches for task distribution and hyperparameter optimization, often leveraging fuzzy logic or deep state-space models for improved accuracy and adaptability. These advancements are crucial for applications ranging from healthcare monitoring and traffic safety analysis to neuromorphic computing and industrial IoT systems, enabling faster, more accurate, and resource-efficient decision-making.