Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India

Research Article

Investigation on Evaluation and Optimization of Intelligent Business Statistical Models

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  • @INPROCEEDINGS{10.4108/eai.17-11-2023.2342805,
        author={Qihan  Bao},
        title={Investigation on Evaluation and Optimization of Intelligent Business Statistical Models},
        proceedings={Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development, ICSETPSD 2023, 17th-18th November 2023, Coimbatore, Tamilnadu, India},
        publisher={EAI},
        proceedings_a={ICSETPSD},
        year={2024},
        month={1},
        keywords={intelligent business statistical model business intelligence technology data center data analysis enterprise management},
        doi={10.4108/eai.17-11-2023.2342805}
    }
    
  • Qihan Bao
    Year: 2024
    Investigation on Evaluation and Optimization of Intelligent Business Statistical Models
    ICSETPSD
    EAI
    DOI: 10.4108/eai.17-11-2023.2342805
Qihan Bao1,*
  • 1: University Of Calgary, Calgary, Canada
*Contact email: hardenbao123@gmail.com

Abstract

In network management, especially when a large amount of business data is accumulated in daily operations, the application effect of Business Intelligence (BI) technology in network management is particularly significant. Based on the business analysis process involved in the data analysis of Company A’s data center, this article summarized the problems existing in the existing data analysis system, drew inspiration from the principles and processes of BI technology, and used BI technology to rebuild the business analysis system solution for Company A’s data center. A business analysis system model based on metadata management was proposed, which could provide analysis results and visual reports for business decision makers and business analysis teams. It focused more on effectively explaining the current business situation and providing valuable information or knowledge for enterprise decision-making. According to the analysis of the distribution proportion of visiting regions, it could be found that South China accounted for 23.25% and North China accounted for 12%. The South China region had a strong interest in Company A’s business. By utilizing intelligent business statistical models, the potential value contained in enterprise data could be better excavated, thereby providing guidance for the operation and decision-making of enterprises.