Optimization design of computer information processing systems in the big data perspective

Computer information processing systems face numerous challenges in the context of big data, and optimization design provides an important avenue to enhance system performance in handling large datasets. This paper first analyzes the bottlenecks, inefficiencies, and poor scalability challenges that big data poses to existing systems. Subsequently, it proposes three system optimization design approaches: modular design, pipeline parallel processing, and elastic scaling mechanisms. Specific solutions such as modular segmentation, MapReduce pipeline implementation, and dynamic resource allocation are presented based on these optimization ideas. These optimization strategies and design solutions effectively enhance the system's scalability and processing performance in the face of big data. Therefore, this paper offers important insights and approaches to drive the evolution of computer information processing systems into the era of big data.