Research on information visualization for digital media design methodology based on big data technology

Abstract

Abstract Information visualization is one of the important contents of digital media design, and they have a mutually complementary relationship. This paper combines the realistic background of the big data era and makes a more comprehensive introduction to the theoretical study of data visualization, including visual perception, visualization functions, judgment indexes, and principles. Secondly, by collecting the time series data, anomaly detection is carried out in the pre-processing stage, while the ring test is divided into a short-term ring and a long-term ring because the time series carries the property of time succession. Finally, anomaly detection is performed by its statistical features, which include distribution, moving average, exponential smoothing, standard deviation, and mean, whose values are equivalent to observing single-dimensional data when the sample data distribution is used as a perspective. The results show that through the comparative analysis of data visualization and traditional mathematical statistics, the correlation coefficients of information visualization for digital media design are between 0.8003 and 0.8129, while the traditional statistical methods for digital media design are only between 0.5038 and 0.5523. The information visualization proposed in this paper is better for digital media design to convey the art expressed by data and deeper mining analysis of spatiotemporal data.