EMIRANDA LOKA, ALDA KIKA
Abstract
The rise of massive, heterogeneous, and rapidly evolving datasets has amplified the need for intelligent visualization systems capable of supporting insight extraction, interpretability, and decision-making at scale. Traditional visualization approaches face limitations when confronted with high- dimensional, unstructured, or streaming big data. Recent developments in artificial intelligence (AI), machine learning (ML), and analytical modeling offer new techniques to automate and enhance data visualization pipelines. This paper presents a comprehensive review of big data visualization augmented by AI and analytical techniques, emphasizing recent advances from 2022–2025, including generative AI for visualization automation, Explainable AI (XAI) integration, human–AI collaboration, and scalable ML- driven frameworks. Challenges and future directions are also identified, linking technological progress with emerging needs in interpretability, scalability, and ethical visualization practices.
Key words: Big Data, Data Visualization, Artificial Intelligence (AI), Machine Learning (ML).