Current Volume 9
Generative AI for Creative Data Management: Optimizing Database Systems in the Creative Industry
Abstract
the creative industry relies on vast and complex datasets, including digital assets, multimedia content, and evolving creative trends. Traditional database management systems struggle to keep pace with the dynamic and unstructured nature of this data. Generative AI offers an innovative solution by automating metadata tagging, optimizing content retrieval, and enhancing data-driven decisionmaking. This paper explores how generative AI models, including large language models (LLMs) and generative adversarial networks (GANs), improve data structuring, trend prediction, and asset management in creative domains such as media, entertainment, and design. By integrating generative AI with database systems, organizations can streamline workflows, reduce manual intervention, and unlock new opportunities for innovation. The study presents case studies and experimental analyses to demonstrate the impact of AIdriven data management on efficiency, scalability, and creative output. Through intelligent automation and optimization, generative AI reshapes the way the creative industry organizes, accesses, and utilizes its digital assets.
Citations
IRE Journals:
Oluwafemi Oloruntoba
"Generative AI for Creative Data Management: Optimizing Database Systems in the Creative Industry" Iconic Research And Engineering Journals Volume 7 Issue 7 2024 Page 588-597
IEEE:
Oluwafemi Oloruntoba
"Generative AI for Creative Data Management: Optimizing Database Systems in the Creative Industry" Iconic Research And Engineering Journals, 7(7)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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