Data catalogs are essential tools for managing and discovering organizational data, but traditional navigation methods can be cumbersome, especially as data volumes grow. This paper explores the integration of generative AI-based conversational interfaces to enable intuitive and efficient navigation of unified data catalogs. By leveraging advanced natural language processing (NLP) models, users can interact with data catalogs through human-like dialogues, eliminating the need for complex query languages and manual search processes. We discuss the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries. Through a case study and usability testing, we demonstrate how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes. Finally, we address the challenges and future directions of deploying generative AI in data catalog systems, emphasizing the need for scalable and secure solutions.
Steven Young, Rebecca Green· International Journal of Dat...· 0 citations
Predictive intelligence enables systems to forecast future events using historical data, domain knowledge, and advanced analytics. Traditional approaches are either knowledge-driven, offering interpretability and reasoning, or data-driven, providing strong learning capabilities but facing challenges in explainability and adaptability. Hybrid predictive intelligence combines both paradigms to overcome their limitations. The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support. By integrating expert knowledge with machine learning techniques, it improves prediction accuracy, reliability, transparency, and decision-making. Applications span healthcare, industrial automation, cybersecurity, finance, smart cities, and intelligent transportation systems. Comparative studies show that hybrid models outperform conventional approaches in accuracy, robustness, and interpretability. Future developments in federated learning, digital twins, graph neural networks, and autonomous reasoning are expected to further enhance predictive intelligence for next-generation intelligent systems.
Karen Lewis, Steven Young· International Journal of App...· 0 citations