Research on Integration of Intelligent Algorithms in Big Data-Driven Decision Support Systems

This study aims to explore the integrated application of intelligent algorithms in big data-driven decision support systems, with special attention to the introduction of edge algorithms to improve the real-time performance and efficiency of the system. A novel edge computing architecture is proposed, which allows initial data processing and analysis near the source of data generation, thereby reducing the burden on the central server and speeding up the decision-making process. By integrating edge algorithms, the system can process real-time data flow more efficiently and provide immediate decision support. In order to verify the performance of the proposed system, extensive system simulation experiments are carried out in this paper. The simulation results show that compared with the traditional centralized DSS, the integrated intelligent algorithm and edge computing system has a significant improvement in processing speed, accuracy and resource consumption. Especially when dealing with large-scale data sets and complex decision scenarios, the system shows superior adaptability and robustness.