Stream Analytics

Stream analytics focuses on processing continuous data streams in real-time to extract insights and make immediate decisions. Current research emphasizes developing efficient and interpretable algorithms, such as ensemble methods and variations of support vector machines and random forests, to handle concept drift (changing data distributions) and detect anomalies within these streams. This field is crucial for applications across diverse sectors, including finance (fraud detection), healthcare (predictive modeling), and industrial IoT (network monitoring), where timely analysis of large volumes of data is essential for improved efficiency and decision-making. The development of robust, scalable, and explainable models remains a key focus.