Intelligent Big Data Models for Next Generation Applications

Hideyuki Nakanishi

Intelligent Big Data Models for Next Generation Applications

Author(s)	Prof. Hideyuki Nakanishi
Country	Japan
Abstract	The rapid expansion of digital technologies has generated unprecedented volumes of structured, semi-structured, and unstructured information, creating both opportunities and challenges for next-generation applications. Traditional data-processing architectures are increasingly limited in their ability to manage high-volume, high-velocity, and heterogeneous datasets while simultaneously producing intelligent insights. Intelligent Big Data models integrate advanced analytics, machine learning, artificial intelligence, distributed computing, predictive modeling, and real-time processing to transform complex datasets into actionable intelligence. This study examines intelligent Big Data models for next-generation applications and proposes a conceptual framework that integrates data acquisition, distributed storage, preprocessing, intelligent analytics, predictive modeling, decision support, and continuous feedback. A conceptual and descriptive methodology was adopted through a thematic examination of scholarly literature concerning Big Data analytics, artificial intelligence, machine learning, cloud computing, edge computing, intelligent information systems, and data-driven applications. The analysis indicates that intelligent Big Data models can improve scalability, analytical accuracy, automation, responsiveness, personalization, and decision-making across emerging application environments. Real-time analytics and edge computing further support applications that require rapid responses, including smart cities, connected healthcare, intelligent manufacturing, financial services, autonomous systems, and digital platforms. The proposed model emphasizes a continuous analytical cycle in which data are collected from multiple sources, integrated and processed through scalable architectures, analyzed using intelligent models, converted into application-level insights, and evaluated through feedback. Despite their potential, intelligent Big Data systems face challenges related to data quality, computational complexity, interoperability, privacy, cybersecurity, model interpretability, infrastructure costs, and skilled human resources. The findings suggest that next-generation applications require architectures capable of combining scalable data management with intelligent analytical capabilities. The study concludes that intelligent Big Data models can provide a strategic foundation for developing adaptive, predictive, and responsive digital applications when advanced technologies are supported by effective governance, security, human oversight, and continuous model evaluation.
Keywords	Big Data, Intelligent Models, Artificial Intelligence, Machine Learning, Big Data Analytics, Next-Generation Applications, Predictive Analytics, Distributed Computing, Edge Computing, Intelligent Systems
Field	Engineering
Published In	Volume 2, Issue 4, July-August 2020
Published On	2020-08-31

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American International Journal of Advanced Research and Development

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Intelligent Big Data Models for Next Generation Applications

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