Chapter 2. Principles of Data Visualization

Information visualization aims at visually representing different types of data (e.g., geographic, numerical, text, network) in order to enable and reinforce cognition. Information visualization offers intuitive ways for information perception and manipulation that essentially amplify the overall cognitive performance of information processing, especially for non-expert users. Visual analytics combines information visualization with data exploration capabilities. It enables users to explore and analyze unknown (in terms of semantics and structure) sets of information, discover hidden correlations, and causalities and make sense of data in ways that are not always possible with traditional quantitative data analysis and mining techniques. This is of great importance, especially given the massive volumes of digital information concerning nearly every aspect of human activity that are currently being produced and collected. The so-called Big Data era refers to this tremendous volume of information collected by digital means and analyzed to produce new knowledge in a plethora of scientific domains.
The Linked Open Data cloud is one of the main pillars of the so-called Big Data era. The number of datasets published on the Web, the amount of information and the interconnections established between disparate sources being available in the form of Linked Open Data are nowadays ever expanding, making traditional ways of analyzing them insufficient and posing new challenges in the way humans can explore, visualize and gain insights out of them.
In this chapter, we present the basic principles and tasks for data visualization. We first provide the tasks for the preparation and visualization of data. We then present the most popular ways for graphically representing data according to the type of data and then we provide an overview of the main techniques for users to interact with the data. Finally, we show the main techniques used for visualizing and interacting with Big Data.

Sections
  • 2.1   Data Visualization Design Process
  • 2.2   Data Visualization Types
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          − Visualizing Patterns over Time
          − Visualizing Proportions
          − Visualizing Graph Relationships
          − Visualizing Data on Maps
  • 2.3   Interactive Visualization
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  • 2.4   Visualization in Big Data era
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          − How does the Visualization of Big Data Differs from Traditional Ones?
          − Visualization Systems and Techniques
  • 2.5   Conclusions
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