Treffer: Intelligent Edges: Mapping the Future Convergence of Edge Computing and Big Data Analytics.

The changing scene of edge computing is causing a revolution in technology, yet significant research gaps exist in understanding its full potential and implementation challenges. Through a systematic analysis of 78% of recent literature, combined with quantitative performance evaluations and real-world case studies, this research addresses critical gaps in architectural frameworks, standardization protocols, and implementation metrics. Using a three-phase methodology combining systematic literature review, qualitative trend analysis, and quantitative performance assessment, our research reveals significant improvements in operational efficiency. The systematic review identified key architectural patterns, while performance testing demonstrated a 65% reduction in response time, 72% less data transfer requirements, and 99.95% system uptime. Through comparative analysis of edge-deployed AI models, we validated new lightweight architectures that are 75% smaller while maintaining 95% accuracy. Our federated learning experiments established effective privacy-preserving methods, successfully balancing data utility with compliance requirements. Industry case studies, conducted through rigorous field testing and performance monitoring, validated our findings in manufacturing and healthcare sectors. In manufacturing, controlled experiments achieved 99.3% accuracy in defect detection, while healthcare implementations demonstrated remote patient monitoring with sub-50ms latency. Costbenefit analysis of these implementations revealed 35-45% reduced operational costs and ROI improvements up to 285% across sectors. Employing predictive modeling and trend analysis techniques, our research projects significant evolution within the next decade: 85% improvement in system autonomy, 3.5× enhancement in processing power, and 65% gains in energy efficiency. Edge computing, defined as "computation happening anywhere near the data sources and control," continues to evolve, addressing emerging challenges in distributed computing architectures. [ABSTRACT FROM AUTHOR]