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Key Scientific Research Problems on E-commerce Big Data

In: Handbook of E-commerce in China

Author

Listed:
  • Jie Cao

    (Hefei University of Technology)

  • Da Ding

    (School of Management, Hefei University of Technology)

Abstract

E-commerce big data fusion aims to integrate and analyze massive amounts of data from various sources to help enterprises understand market dynamics, mine customer interests, and predict development trends. This facilitates intelligent decision-making in e-commerce enterprises, enhances service quality, improves production efficiency, reduces business risks, and maintains competitive advantages. The process comprises three major steps: data preprocessing, data integration, and knowledge fusion. Data preprocessing involves steps such as data cleaning, integration, reduction, and transformation to improve data quality, optimize the data analysis workflow, economize analysis time and resources, and enhance decision-making quality. Additionally, e-commerce big data fusion requires clarifying data ownership, access rights, and compliance issues, selecting appropriate data sources, data models, and analysis methods to ensure data reliability and validity. Cross-border linkage and multivariate interaction are also key areas of research in e-commerce big data, aimed at discovering potential cross-industry connections, creating new business value, and enhancing user experience.

Suggested Citation

  • Jie Cao & Da Ding, 2025. "Key Scientific Research Problems on E-commerce Big Data," Springer Books, in: Zheng Qin & Qinghong Shuai (ed.), Handbook of E-commerce in China, chapter 30, pages 577-617, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-7629-3_30
    DOI: 10.1007/978-981-96-7629-3_30
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