AI-Driven Architectures and Algorithms for Secure and Scalable Big Data Systems

The exponential growth of data from domains such as healthcare, finance, education, and the Industrial Internet of Things has necessitated the development of intelligent and scalable big data systems. Traditional methods often fall short when confronted with real-time processing, heterogeneity, and the scale of modern data. Artificial intelligence—especially neural networks, large models, and distributed algorithms—has become essential in processing and extracting value from big data. At the same time, security, privacy, and resource efficiency remain central challenges. Incorporating flexible optimization techniques, including metaheuristics and complex system modeling, provides additional robustness for dynamic, large-scale applications. These interdisciplinary approaches are essential for building next-generation data infrastructures.

This Research Topic seeks to explore the intersection of artificial intelligence and big data systems, focusing on intelligent architectures, efficient algorithms, and secure infrastructure. We aim to collect innovative research on distributed and parallel computing systems, resource management techniques, and scalable data processing frameworks. Contributions involving multimodal data—such as text, images, audio, time-series, and graphs—are particularly encouraged, especially when supported by deep learning or large AI models. While the focus is on AI-driven methods, we also welcome studies that leverage optimization strategies, including metaheuristic algorithms, to solve complex system-level challenges in scheduling, caching, and network management. Security and privacy protection, especially in large model environments or mobile data contexts, is another key area of interest. The goal is to promote intelligent, robust, and secure solutions applicable across various industries.

We welcome submissions addressing, but not limited to, the following areas:

- Architecture and systems for scalable big data processing
- AI techniques for multimodal and streaming data
- Distributed, parallel, and resource-efficient algorithms
- Privacy and security in large-scale AI and mobile data systems
- Applications in healthcare, education, finance, industrial IoT, and e-commerce
- Optimization techniques, including metaheuristics, for system performance and resource management
- Network communication systems tailored for data-driven applications

We are interested in theoretical models, system implementations, as well as application-driven case studies. Authors are encouraged to submit original research, reviews, or methodological papers that contribute to the design and advancement of intelligent big data systems.