Title: Leveraging traditional business culture for business intelligence: a scalable parameter server architecture with distributed machine learning

Authors: Chengcai Xing

Addresses: College of Business Administration, Zhengzhou University of Science and Technology, Zhengzhou, 450064, Henan, China

Abstract: Yan'an, a significant historical and cultural hub in China, is being revitalised and utilised to drive development in various spheres. The city's traditional commercial and cultural resources are being harnessed to contribute to its political, cultural, educational, and economic growth. Yan'an models other historically significant regions, demonstrating how heritage can be leveraged for contemporary development. Advanced machine learning approaches are used to overcome scalability and robustness issues in large-scale data-driven systems. The parameter server architecture decentralises the training process of machine learning models, enabling efficient handling of vast datasets and high computational demands. This design enhances fault tolerance and ensures seamless operation under challenging conditions. Intelligent simulations and tests validate the efficacy of these machine-learning approaches in modelling the evolution and application of traditional commercial culture. These simulations provide a dynamic and accurate representation of how cultural and business practices can adapt and thrive in modern contexts. The reliability and precision of machine learning models in capturing complex patterns and trends inherent in cultural and economic transitions are underscored through rigorous testing. This exploration highlights the innovative intersection of technology and tradition, showcasing how machine learning can play a transformative role in preserving and advancing historical and cultural assets.

Keywords: traditional commercial and cultural resources; Yan'an historical value; distributed machine learning; parametric server architecture.

DOI: 10.1504/IJBIDM.2026.152465

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.2/3, pp.243 - 259

Received: 01 Mar 2024
Accepted: 06 Jan 2025
Published online: 23 Mar 2026 *

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