This track focuses on the latest methodologies and technologies in predictive analytics specifically tailored for big data environments. Researchers are encouraged to present innovative approaches that enhance forecasting accuracy and decision-making processes.

This session will explore various machine learning techniques that facilitate intelligent data processing in large-scale systems. Contributions should highlight novel algorithms and their applications in real-world scenarios.

This track addresses the challenges associated with cloud-based analytics in big data systems, including scalability and security concerns. Papers should propose solutions that enhance the efficiency and reliability of cloud analytics.

This session aims to discuss AI frameworks that streamline data integration and management processes in big data systems. Submissions should focus on frameworks that improve data accessibility and usability across diverse platforms.

This track invites papers that present innovations in scalable computing architectures designed for big data applications. Emphasis will be placed on performance optimization and resource management strategies.

This session will cover the role of automation in enhancing IT infrastructure to support big data systems. Researchers are encouraged to share insights on automated processes that improve operational efficiency and reduce human error.