In-situ visualization is divided into image, distribution, compression and features.

Visualizations generated during numerical simulation are used to alleviate large-scale numerical simulation output bottlenecks. Output is in-situ visualization of images, which maps data to visualization and saves it as images during numerical simulation. Output is in-situ visualization of distributed data, based on user-defined statistical metrics, which are calculated and saved during the numerical simulation process, and subsequent visualization of statistical data.

The in-situ visualization whose output is compressed data uses compression algorithms to reduce the output scale of numerical simulation data, and the compressed data is used as the input for subsequent visualization; the in-situ visualization whose output is features uses the feature extraction method to extract features and save them during numerical simulation, and the feature data is used as the input for subsequent visualization.

Principles of Data Visualization:

Data Visualization SystemsData visualization is the practice of transforming information into a visual context (e.g., a map or a graphic) in order to make it easier for the human brain to understand the data and derive insights from it. The primary goal of data visualization is to more easily identify patterns, trends, and outliers in large data sets. The term is often used interchangeably with other terms, including information graphics, information visualization and statistical graphics.

Data visualization is one of the steps in the data science process that states that after data has been collected, processed, and modeled, it must be visualized in order to draw conclusions. Data visualization is also an element of the broader discipline of Data Representation Architecture (DPA), which aims to identify, locate, manipulate, format, and deliver data in an efficient manner.

As data visualization vendors extend the capabilities of these tools, they are increasingly being used as a front-end for more complex big data environments. In this context, data visualization systems can help data engineers and scientists track data sources and perform basic exploratory analysis on data sets before or after more detailed advanced analysis.

Reference for the above: Baidu Encyclopedia-Data Visualization