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Adaptive Visualization Framework for Real-Time User Engagement in Big Data
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Author(s): Guangda Li (Beijing Information Science and Technology University, China), Qiong He (Beijing Information Science and Technology University, China)and Xiaoyan Gu (Beijing Information Science and Technology University, China)
Copyright: 2026
Volume: 22
Issue: 1
Pages: 17
Source title:
International Journal of e-Collaboration (IJeC)
Editor(s)-in-Chief: Jingyuan Zhao (University of Toronto, Canada)
DOI: 10.4018/IJeC.402015
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Abstract
With the growth of data and the diversification of users' needs, traditional dashboards cannot support real-time interaction and multiple opinions. In this paper, a double closed-loop framework of “user perception-component scheduling” is proposed, which combines semantic-behavior fusion and Bandit-Mermaid algorithm to dynamically optimize the visualization of big data. An 8-week experiment with 3.8 TB logs and 1250 surveys shows that the delay is reduced by 97 milliseconds, the attention is increased by 28.4%, and the task completion rate is improved by 16.2%. There is a linear positive correlation between scheduling frequency and satisfaction (r = 0.41, P < 0.01). Bandit parameter ε = 0.06 optimizes the stability and click rate. The research results show that the framework can be applied to financial monitoring, e-commerce, and smart cities and provides a replicable model for adaptive visualization.
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