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This text discusses a field of visualization and selected implementations of visualization techniques that involve graphics and visuals, mostly described as data visualization, information visualization (IV), and knowledge visualization (KV). At present, visualization means using the computer, which transforms data into information, and then visualization converts information into picture forms and creates graphic images and symbols to convey and express meaning; this lets us comprehend data and make decisions. Thus communication through visualization is at the same time pictorial and linguistic. It is socially and culturally conditioned, based on familiar linguistic patterns, as in a ‘pie chart’ metaphor for market shares or a ‘starry night’ metaphor showing data in 3D (Bertschi & Bubenhofer, 2005).
The cross-disciplinary, interactive culture of knowledge visualization requests that the visual content analysis would be applied to data and knowledge. Visualization enhances communication through information display with the use of letters, numerals, art, graphic design, visual storytelling, signs, symbols, and application software. Drawing basic shapes like squares, triangles, and circles connected by lines and arrows, and then inserting simple drawings inside of these shapes creates visualization of our concepts. Graphs, diagrams, or animations can visualize messages as well. Examples of the non-visual creations are multimodal interactive data presentations, sonifications, and haptic/touch interfaces, for example pressure sensitive interfaces. As productive thinking in whatever area of cognition takes place in the realm of imagery, visual perception should be considered a cognitive activity. Visualization has also been considered a semiotic process because of the use of signs to present ideas.
The object of this article is to present selected concepts, methods, and tools related to visualization of data, information, and knowledge. The further text presents selected approaches to the concept of visualization and the ways it mediates between the user and the physical world. It overviews selected methods and tools such as data mining, clustering technique, concept mapping, knowledge maps, network and web-search result visualization, open source intelligence, visualization of the semantic web, visual analytics, and tag cloud visualization.
Key Terms in this Chapter
Pattern: The regular order existing in nature or in a manmade design. In nature patterns can be seen as symmetries (e.g., snowflakes) and/or structures having fractal dimension such as spirals, meanders, or surface waves. In computer science, design patterns serve in creating computer programs. In the arts, pattern is an artistic or decorative design made of recurring lines or any repeated elements. We can see patterns everywhere in nature, mathematics, art, architecture, and design. A pattern makes a basis of ornaments, which are specific for different cultures.
Algorithm: A mathematical sequence of instructions telling how to carry on computation to implement it as a program. Algorithms are used to create repetition by performing operations on previous products. Algorithm serves for solving a complex problem by writing a sequence of simpler, unambiguous steps. Such course of action is used for writing computer programs and in programmed learning.
Information Aesthetics: Forms a cross-disciplinary link between information visualization and visualization art.
Knowledge Visualization: Uses visual representations to transfer insights and create new knowledge in the process of communicating different visual formats.
Infographics: Tools and techniques involved in graphical representation of data, mostly in journalism, art, and storytelling.
Information Visualization: Often characterized as representation plus interaction, means the use of computer-supported, interactive visual representations of abstract data to amplify cognition and derive new insights.
Concept Map: A graphical two-dimensional display of knowledge. Concepts, usually presented within boxes or circles, are connected by directed arcs that encode, as linking phrases, the relationships between the pairs of concepts.
Data Visualization: Information abstracted in a schematic form to provide visual insights into sets of data. Data visualization enables us to go from the abstract numbers in a computer program (ones and zeros) to visual interpretation of data. Text visualization means converting textual information into graphic representation, so we can see information without having to read the data, as tables, histograms, pie or bar charts, or Cartesian coordinates.
Scientific Visualization: Presents real, abstract, or model-based objects in a digital way directly from the data. It may present the art-science cooperative learning projects and make knowledge comprehensible to a wide audience. Visualization as storytelling comprises narratives, interactive graphics, explanatory and animated graphics, and multimedia.