Streaming Algorithm

Streaming algorithms efficiently process continuous data flows, aiming to extract insights or perform computations with limited memory and processing time per data point. Current research emphasizes developing robust and efficient algorithms for diverse applications, including high-dimensional data clustering, online prediction of time series, and real-time recommendation systems, often employing techniques like sparse representations, Gaussian processes, and k-means clustering within streaming frameworks. These advancements are crucial for handling the ever-increasing volume of data generated by various sources, enabling real-time analysis and decision-making in fields ranging from machine learning and signal processing to network analytics and personalized recommendations.