## 2 Background Work
### Data Visualization  
Data visualization is a research field that studies how data can be visualized to be better understood by humans. It is known that for the human eye, it is easier to spot differences in shapes, for instance, what line is the longest or what colours are included in a canvas rather than in a table of numbers.  
Therefore, data visualization can be traced back to the beginning of civilization when tables with the position of the stars and other celestial objects were used. Geometric diagrams have also been used for many centuries, as well as exploration and navigation maps [33]. In the 17 th century, coordinates systems, analytic geometry, and theories of errors of measurement ([PERSON], [PERSON], [PERSON]) made their initial steps. Later in the 18 th century, with the help of statistics, more information started to appear on maps as economics. At the start of the 19 th century, modern graphics were established with the first histograms, bars, pie charts, and more. During the same century, the first 3-D surface plot appeared. The following century started with limited advancements only to give space in the second half that the leaf plots and boxplots materialized, better organization of data based on visual and perceptual elements, and finally, the FORTRAN programming language was developed which was the first high-level programming language. Since 1975 and after, data visualization has been a complete research area.  
Many disciplines are contributing to data visualization [34]; one is psychology, which studies the impact of shapes, colours, sizes, and the perception of data. Computer science and statistics help to develop new techniques and technologies to handle data, such as machine learning and data mining. Additionally, infographics and dashboards are created using graphical and multimedia techniques using different shapes, colours, scales, and data.  
The graphical representation of data makes it easier for the human eye to interpret the information. There are many techniques and diagrams to better suit the data that needs to be displayed. As stated before, shapes, colours, and sizes can help humans to understand the data, but the overuse of colours might be misleading, so it needs careful treatment to ensure the correct meaning is communicated. Another aspect that needs attention is highlighting unimportant information that can destruct from the critical data on the diagrams [34].  
As data visualization is widely used nowadays, from marketing and economy to demographics, an increasing number of people are involved in this field. That leads to new challenges that need further study [35]. The first significant challenge is that people that need to use or produce data visualizations can have very varied backgrounds. Thus, there is a need for tools that will make it possible for people to create and understand data visualization from heterogeneous datasets that might contain noise or bad quality entities without the need for robust data science or I.T. background. Additionally, there is a need for tools that will allow data scientists to perform data exploration without requiring data manipulation or analytics skills.  
Furthermore, as the data are increasing, a necessity for new techniques to provide fast results has arisen. For closure, with the advancements of artificial intelligence and machine learning, research on how to capitalize on those techniques to provide better graphs depending on the content of the data is promising. A cheat sheet for data visualization techniques is presented by [PERSON] et al. [36].