The Convergence of Big Data Analytics and Predictive Modeling in Global Socio-Technical Systems: A Comprehensive Framework for Intelligent Management and Sustainable Innovation
Keywords:
Big Data Analytics, Predictive Modeling, Autonomous Vehicles, Sustainable DevelopmentAbstract
The rapid proliferation of digital infrastructure has ushered in an era defined by the ubiquity of big data and the necessity of predictive analytics. This research provides an exhaustive examination of how big data analytics (BDA) and predictive modeling (PM) are fundamentally restructuring various sectors, including autonomous transportation, humanitarian logistics, e-commerce, healthcare, and finance. By synthesizing diverse theoretical frameworks-ranging from network calculus for autonomous vehicles to probabilistic finite automata for niche process optimization-this study elucidates the mechanisms through which intelligent management is achieved in smart city environments. The research investigates the role of BDA in enhancing collaborative performance and fostering sustainable consumption and production behaviors, addressing the urgent global need for environmental and social sustainability. Furthermore, the article explores the technical foundations of data processing, including symbolic representation of time series, kernel-based non-parametric regression, and principal component analysis, to provide a holistic view of the analytical pipeline. The findings suggest that while BDA offers unprecedented opportunities for profit maximization and operational efficiency, its success is contingent upon the development of lightweight, anonymous authentication schemes to protect sensitive information, particularly in medical and governmental contexts. This article serves as a seminal synthesis for researchers and practitioners, offering a detailed roadmap for leveraging artificial intelligence to drive the next wave of industrial and societal innovation.
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