Application and Evaluation of Real-Time Analysis Algorithms in Dynamic Data Processing Systems

Dynamic data processing systems are key technologies in modern data-driven applications, widely used in fields such as the Internet of Things, finance and healthcare. This study explores the application and evaluation of real-time analytical algorithms for dynamic data processing. An efficient algorithmic framework combining adaptive windowing, incremental learning and distributed computing is proposed to achieve low-latency and high-accuracy decision-making capabilities. Through experimental validation in a real data streaming environment, the results show that the proposed algorithm significantly outperforms traditional algorithms with about 35% reduction in latency and 20% improvement in prediction accuracy. The research results in this paper show that the real-time analysis algorithm has a promising application in dealing with dynamic heterogeneous data streams. Meanwhile, the article also discusses the limitations and future optimisation directions of the method, which provides a reference for further research in related fields.