Data Stream

Data stream processing focuses on efficiently analyzing continuous, high-volume data flows, aiming to extract timely insights and adapt to evolving data distributions (concept drift). Current research emphasizes developing robust algorithms and model architectures, such as incremental learning methods (e.g., adaptive random forests), online anomaly detection techniques (e.g., model-based approaches), and federated learning strategies for distributed data streams, often incorporating generative models for data augmentation or replay. This field is crucial for real-time applications across diverse domains, including fraud detection, industrial monitoring, and robotics, where immediate analysis of continuous data is essential for effective decision-making.