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

Zhaoxia Zheng

doi:10.1117/12.3026496

27 March 2024 Optimization design of computer information processing systems in the big data perspective

Zhaoxia Zheng

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131050E (2024) https://doi.org/10.1117/12.3026496
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhaoxia Zheng "Optimization design of computer information processing systems in the big data perspective", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131050E (27 March 2024); https://doi.org/10.1117/12.3026496

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