Practical Data Monitoring

Practical data monitoring focuses on developing and deploying robust systems for real-time analysis of large-scale datasets, aiming to detect anomalies and unexpected behavior across diverse applications. Current research emphasizes lightweight, unsupervised methods, often employing machine learning techniques like deep neural networks, to efficiently process streaming data and adapt to dynamic environments. This field is crucial for improving the reliability and performance of complex systems, ranging from large-scale computing infrastructure to smart city applications and healthcare monitoring, ultimately enhancing efficiency and safety.