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Data Asset Valuation and Security: A Distributed Machine Learning Approach

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Publicado en:International Journal of E-Collaboration vol. 21, no. 1 (2025), p. 1-25
Autor Principal: Yang, Ling
Outros autores: Zhang, Yihong, Feng, Ye, Xue, Jun, Dong, Danhuang, Luo, Zhejun
Publicado:
IGI Global
Materias:
Data processing
Experiments
Security
Classification
Competition
Internet
Machine learning
Energy consumption
Companies
Trade
Data quality
Assets
Electronic commerce
Competitiveness
Valuation
Quality assessment
Business competition
Innovations
Morality
Image classification
Data integrity
Acceso en liña:Citation/Abstract
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Resumo:With the development of Internet technology, the amount of enterprise data has surged and become a core asset. In e-commerce, data security not only protects data, but also affects the ability of transforming data into assets, thus enhancing the competitiveness of enterprises. This study proposes a system framework based on distributed machine learning to enhance data integrity and network security. The system integrates advanced algorithms, effectively processes large-scale data sets, and performs well in cloud image classification tasks. Experiments show that the energy consumption of the system is reduced by 68.92% and the task processing speed is increased by 4.67 times. In addition, the data quality evaluation model is studied to provide decision support for e-commerce enterprises. These innovations provide a new perspective for data asset evaluation and security and also support the application of distributed machine learning in e-commerce.
ISSN:1548-3673
1548-3681
DOI:10.4018/IJeC.392027
Fonte:ABI/INFORM Global