This presentation discusses visual analytics, which combines visualization techniques with analytical reasoning to enable knowledge discovery in large datasets. It outlines the goals of visual analytics like presenting data understandably, analyzing and deriving insights from large datasets. Specific aspects covered include temporal and geospatial visualization, plagiarism detection, social networks, and scientific collaboration. Frameworks for visual analytics like OpenGL, Gephi and Gapminder are also described. Potential applications that could be implemented include geospatial and temporal visualization of research institutions, plagiarism detection visualization, and bibliographic coupling analysis.

1. Visual Analytics
Ksenia Kharadzhieva

2. Structure of the Presentation
● Visualization and integrated disciplines
● Goals of visual analytics
● Aspects of visual analytic, relevant to our PG
● Tools and frameworks for visual analytics
● What can be implemented?

3. Integrated disciplines
[1]

4. Goals of Visual Analytics
● presentation of data in an understandable way
● analysis of large datasets
● derivation of relevant data from large datasets
● discovering hidden information, patterns, trends
● providing instruments for interaction with data

5. Considered aspects of Visual Analytics
● Space and time visualization
● Plagiarism visualization
● Visualization of social networks
● Visualization of scientific collaboration
● Perception and cognitive aspects

6. Temporal and Geospatial Visualization
● Geospatial data is different from usual statistical data.
● Toblers first law: "everything is related to everything else,
but near things are more related than distant things".
● Data is often uncertain: errors, missing values, deviations.
● Hierarchical scale of time; different types of time: linear and
cyclic, branching and multiple perspectives.
[1]

7. Space-time cube
[1]

8. Linear and cyclic representation
[1]

9. Plagiarism Visualization
[9]

10. Plagiarism Visualization
[9]

11. Visualization of Social Networks
[2]

12. Visualization of Social Networks
[3]

13. Visualization of Scientific
Collaboration
[4]

14. Perception and Cognition
● "Visual perception is the means by which people interpret
their surroundings and for that matter, images on a computer
display".
● "Cognition is the ability to understand this visual
information, making inferences largely based on prior
learning".
● "Knowledge of how we ’think visually’ is important in the
design of user interfaces."
[1]

15. Perception and Cognition
[1]

16. Perception and Cognition
[1]

17. Libraries and Frameworks
for Visualization

18. OpenGL
● "OpenGL (for Open Graphics Library) is a software
interface to graphics hardware."
● Interface: a set of several hundred procedures and functions
● Enables specifying the objects and operations for producing
high-quality graphical images
[6]

19. OpenGL: Visualization of Contacts in
Twitter
[7]

20. Gephi
● graph and network visualization
● allows to work with complex and
large data sets
● extensive functionality:
importing, visualizing,
spatializing, altering,
manipulating and exporting
● extensibility: tools and fitures can
be added
[8]

21. Gapminder
● Designed to make world
census data available to a
wider audience
● Two-dimentional chart, use
of colour and size
● Allowes the user to explore
the change of the variables
over time
[10]

22. What can we implement?

23. Geospatial and Temporal Visualization
● Nodes represent research
institutions
● Thickness of connection
lines depends on number of
co-authorships
● Enabling change of time
dinamically and observe
changes
● Filtering
[5]

24. Visualization of Plagiarism
● Each page is a little square
● Depending on percentage of
plagiarised content each page has
a colour from green to red
● Opportunity to see percentage of
plagiaism of a chosen page, its
0% 100% contents and used sources

25. Bibliographic Coupling
● If paper cite the same
sources, they are connected
with an arc
● Thickness depends on
number of common citings
● Alternative visualization:
similarity between papers

26. Thank you!

27. References
1. D.A. Keim, J. Kohlhammer, G. Ellis, and F. Mansmann. Mastering the Information
Age - Solving Problems with Visual Analytics. Florian Mansmann.
2. http://www.guardian.co.uk/
3. http://www.facebook.com/
4. Erik Duval Till Nagel. Interactive exploration of geospatial network visualization.
2011.
5. http://maps.google.com/
6. Mark Segal and Kurt Akeley. The opengl graphics system: A specication, 2011.
7. http://uglyhack.appspot.com/twittergraph/
8. https://gephi.org/
9. http://de.guttenplag.wikia.com/wiki/GuttenPlag_Wiki
10.http://www.gapminder.org/