Generative AI for Creative Data Management: Optimizing Database Systems in the Creative Industry

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@article{1707435,
      author = {Oluwafemi Oloruntoba},
      title = {Generative AI for Creative Data Management: Optimizing Database Systems in the Creative Industry},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {7},
      pages = {588-597},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707435.pdf},
      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.},
      month = {January},
  }