Streaming Data

Streaming data analysis focuses on efficiently processing continuous data flows to extract insights and build predictive models in real-time. Current research emphasizes developing online learning algorithms, including those based on incremental gradient methods, Koopman operators, and autoencoders, to handle the dynamic nature of these data streams and address challenges like concept drift and limited memory. These advancements are crucial for applications ranging from anomaly detection in sensor networks to personalized recommendations in online services, enabling more responsive and efficient systems.