Evolving Web Frameworks and Intelligent Query Processing: Integrating Deep Learning and WPM-Based Decision Support in Cloud-Native Software Development
DOI:
https://doi.org/10.15680/IJCTECE.2021.0404004Keywords:
evolving web frameworks, intelligent query processing, deep learning, weighted product method (WPM), decision support systems, cloud-native software development, multicriteria decision-making (MCDM), microservices, Kubernetes, web optimization, query intelligence, explainable AI, DevOps, scalability, real-time adaptabilityAbstract
The continuous evolution of web frameworks and intelligent cloud infrastructures has transformed the way modern software systems are designed, deployed, and optimized. This research proposes a deep learning–driven decision support framework that integrates Weighted Product Method (WPM)–based multicriteria analysis for intelligent query processing and adaptive web development in cloud-native environments. The model leverages advanced deep learning architectures—such as transformers and graph neural networks—to interpret and optimize user queries, automate data routing, and enhance performance in dynamic web applications. The WPM mechanism functions as a decision-support layer, evaluating trade-offs among critical parameters such as latency, scalability, computational cost, and security compliance to ensure optimal development and deployment strategies. Implemented within containerized microservices and orchestrated via Kubernetes, the system demonstrates improved query accuracy, reduced response time, and enhanced adaptability across heterogeneous cloud platforms. The integration of deep learning and WPM-based decision models provides a transparent, scalable, and intelligent pathway for managing complex workflows in full-stack, cloud-native software ecosystems. The findings underscore the framework’s potential to reshape web engineering practices through explainable intelligence, real-time adaptability, and multi-criteria optimization.
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