Data Visualization for Big Data: Exploring Human-Centric Methods for Visualizing Complex Data Sets

The exponential growth of data from various domains necessitates robust and efficient means of interpretation, rendering data visualization an indispensable tool in the realm of big data analytics. This review elucidates the contemporary methods and innovations in human-centric data visualization aimed at managing and interpreting voluminous and complex data sets. Human-centric visualizations prioritize cognitive processes and user interaction, enhancing the ability to discern patterns, correlations, and outliers in data. This paper discusses the evolution of visualization techniques, from traditional static representations to dynamic, interactive models integrating machine learning and artificial intelligence. Special emphasis is placed on techniques such as dimensionality reduction, real-time rendering, and user experience design. Moreover, we address challenges such as scalability, information overload, and contextual relevance. By synthesizing current findings and practices, this article aspires to provide scholars and practitioners with a comprehensive understanding of human-centric approaches to data visualization, thereby fostering efficient extraction of insights from big data.