A Review of the Classification and Application of Big Data Visualization Techniques

Abstract

This article systematically categorizes the fundamental theories and technological developments in data visualization, providing a detailed introduction to the basic visualization methods for various types of data, including spatiotemporal data, geographic information data, time series data, associated data, and text data. Moreover, in light of the characteristics of these different data types, the article will also propose customized visualization solutions. For instance, using step charts and fitting curves can clearly outline the patterns of data changes over time. By exploring the interrelationships among multiple variables through scatter plot matrices and bubble charts, we can uncover the hidden patterns behind these variables more deeply. In addition to the visualization technology itself, this article also incorporates relevant content on data management tools, including distributed storage systems and columnar database management systems. The addition of these tools can further enhance the scalability and efficiency of data processing, providing a more solid underlying support for visualization work. In addition, based on the principles of visual coding and human cognitive laws, this paper also proposes corresponding design criteria to ensure that the introduced visualization methods conform to human perception habits, thereby making the final visualization results more interpretable and more practical.