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/