Optimization of Data Visualization Algorithms in Scalable Artificial Intelligence Systems
DOI:
https://doi.org/10.5281/zenodo.14929767Keywords:
data visualization, artificial intelligence, scalability, dimensionality reduction, interactive visualization, performance optimization, big dataAbstract
The study explores the optimization of AI-driven data visualization algorithms to enhance scalability, interpretability, and computational efficiency. It examines dimensionality reduction techniques such as PCA, t-SNE, and UMAP, highlighting their role in improving data representation. Interactive frameworks like D3.js and Plotly enable real-time data exploration, while performance optimization strategies ensure responsiveness. Security concerns are addressed through encrypted data pipelines and federated learning. Cloud-based solutions enhance cross-platform adaptability. Future research should refine AI visualization techniques, develop standardized evaluation metrics, and improve security frameworks to ensure transparency and efficiency in AI-driven data interpretation and decision-making.
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