Visual Framework Design-sequence, Visual Framework

Visual Framework Design-sequence, Visual Framework
Data Visualization and G2 directory

• Purpose
• What is data visualization?
• Data Visualization stage
• G2 positioning and planning

Purpose

I have been engaged in data visualization for 2 or 3 years. I found that most people's perception of data visualization is to draw charts. Therefore, I want to write some articles on Visual Framework Design. These articles provide a complete set of Visual Framework Design Ideas and implementation details, so that you can have a better understanding of visualization, better visualization in the business.

What is data visualization?

Data Visualization uses data and graphics technology to map information from a data space to a visual space. It is a cross-discipline that spans computer graphics, data science, natural sciences, human-computer interaction, and other fields.

The above domain model is too complex. We pay more attention to data and graphics in data visualization work, thus simplifying the domain model of data visualization:

• Data: focuses on data collection, cleaning, preprocessing, analysis, and mining.
• Graphics: Focus on receiving, extracting information, processing and transformation, pattern recognition, and storage and display of optical images.
• Visualization: Focus on converting data into graphs and performing Interactive Processing

Current knowledge and a field of knowledge that data visualization workers need to master:

 

Data Visualization stage

From the data visualization tools on the market, data visualization can be divided into the following stages:

• Tabulation of data statistics
• Data Result Display
• Data analysis process visualization
• Visualization of virtual reality in VR/AR stage

Tabulation of data statistics

In this phase, traditional statistical charts are used to display data. The typical examples are highcharts, echarts, and other chart libraries. These frameworks have the following advantages:

• The most mature visualization tools include common charts, which are easy to understand and use by developers.
• Low development costs and low requirements on graphics technology and data knowledge

The disadvantages are also obvious:

• Complex Configuration items, poor scalability, and a single chart
• Narrow applicability, poor support for tree and mesh structures, and limited display of data dimensions and data volume

Data result display tools

As data businesses require more and more visualization, the scope of visualization is not limited to statistical charts, but the business needs to display more dimensional data and more diverse graphic display data. Therefore, you need to be able to customize charts based on your needs. Tools at this stage mainly include d3.js, rapheal, and other frameworks. These frameworks provide more detailed graphic tools, more graphic algorithms, advantages of such frameworks:

• Powerful functions, strong interactivity, and wide application scope
• Integrates a large number of graphic algorithms and visual algorithms to reduce the cost of complex charts.
• Strong Community power, a large number of examples

There are also some common problems:

• Requires fine-grained operation graphics, high learning and development costs
• Personalized Requirements and poor reusability

Data analysis visualization tools

The previous tools are based on a prior model and have been used to detect known patterns and rules. Automatic Processing of complex, heterogeneous, and large-scale data often fails, therefore, we need to visualize the data analysis process to better explore patterns and find problems. Currently, the best data analysis visualization tools on the market are tableau and ggplot2 in R. These products have the following advantages:

• It is closely related to data analysis and integrates a large number of data-related algorithms.
• Visualizes the intermediate steps of data analysis.

Disadvantages:

• Strong professionalism, not suitable for entry

Visualization of virtual reality in VR/AR stage

Exploring

G2

The features of currently supported businesses determine the work content of data visualization:

• Basic statistical charts still account for a large proportion of visualization, but development is plagued by the incompleteness of various chart libraries, inconsistent data input and output, and complicated syntax, resulting in poor experience, low development efficiency.
• More and more businesses begin to need to display data in more dimensions. traditional statistical charts cannot meet their needs, and more visualization customization needs.
• Online data analysis services are emerging. traditional PC Tools cannot meet requirements.

We started the development of G2 two years ago. The positioning of G2 is as follows:

• To meet the needs of existing statistical charts, a set of extremely simple graphic syntax is provided to map data from data space to graphic space.
• It provides extended syntaxes for various graphics and supports heterogeneous and complex data types. It is a powerful visualization tool.
• Explores the application of data technology in visualization, and provides data analysis visualization capabilities at the entry point of online data analysis.

Roadmap of G2

G2 has completed the development of 1.0, 1.1, and 1.2. The previous versions have completed the following:

Overall roadMap:

• AntV is a set of practices that we use to support visualized businesses. It helps users use suitable charts in suitable scenarios.
• G drawing library is the bottom layer of data visualization drawing, which needs to be extended in 3D, animation, and interaction.
• G2 1.x has been developed to implement a simple and easy-to-use graphical syntax to meet the needs of statistical charts.
• G2 2. x is under development, hoping to use the data and graphics ing method to shield the details of drawing, so that users can customize personalized visualization requirements, D3 can draw graphics, all should be implemented using simple syntax.
• G2 3.x has previously attempted simple regression, fitting, and other data algorithms. Later, we need to combine them with the data department to construct an online data analysis tool.

Conclusion

Website addresses:

• G2: https://g2.alipay.com/
• Https://antv.alipay.com/
• Github: https://github.com/antvis

In the future, we will publish a series of articles on Visual Framework Design to describe the overall design ideas of G2 and the design details of various syntax elements. You are welcome to participate in our visualization work.