Advancing Data Fusion and Knowledge Discovery Techniques for Modern IT Systems

In the era of big data and complex IT infrastructures, efficient data fusion and knowledge discovery techniques have become essential for optimizing decision-making and system intelligence. Modern IT systems generate vast amounts of heterogeneous data, requiring advanced integration methods to extract meaningful insights. This research explores innovative data fusion frameworks that leverage artificial intelligence (AI), machine learning (ML), and statistical modeling to enhance data integration, reduce uncertainty, and improve real-time analytics. The work proposes a hybrid data fusion approach that combines rule-based, probabilistic, and deep learning models to process structured and unstructured data. Additionally, novel knowledge discovery methodologies are introduced, including graph-based reasoning and automated pattern recognition, to enhance predictive analytics and anomaly detection. The integration of these techniques enables IT systems to enhance cybersecurity, optimize resource allocation, and improve autonomous decision-making in cloud and edge computing environments. Through extensive simulations and real-world case studies, the effectiveness of the proposed techniques is validated in various domains, such as cybersecurity threat detection, IT infrastructure monitoring, and intelligent automation. Results indicate significant improvements in data accuracy, processing efficiency, and system resilience. This research provides a foundation for next-generation IT systems that require robust, scalable, and adaptive data-driven solutions.