Introduction to Information Visualization

1
Introduction to Information Visualization

• Slavomir Petrik, Vaclav Skala

Centre of Computer Graphics and
Visualization University of West Bohemia Plzen,
Czech Republic
2007
2

• Overview of the talk

• History of visualization
• Scientific visualization vs. Information
  visualization
• Concepts, directions and techniques of InfoVis
• 1D, 2D, nD techniques
• Tree and graph-based vis.
• Network structure vis.
• Visualization in InfoVis
• Interacting with visualization

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• From single sketch to tree maps

World map with Babylon in its centre 2300
BC (British museum)
Growing amount of information within a single
image
14th century Roman Britain
1864 Civil war
15th century Leonardo da Vinci
Today Network structure
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• Science of visualization

• Visualization of science vs. science of
  visualization

Scientific visualization
Direct visualization vs. visualization of
structure
Large data
01010101001000100011110010 01001
00100001111001000100 00100011 111001 1000100110
.
Information visualization
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• Areas of interest

• Still not defined precisely !

• Scientific visualization
• deals with direct visualization of data that
  have natural geometric
• structure
• Information visualization
• deals with more abstract data represented by
  trees or graphs
• Visual Analytics
• scientific investigation of the use of
  visualization in sense-making
• and reasoning

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• Information visualization

Examples
Napoleon march into Russia Charles Minard, 1861
Ptolemy world map, 150 AD
Basic concept
Information visualization
Visualization
Data description by structures
Data acquisition
Preprocessing
enrichment, transformation
Highlight selected information
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• Information visualization II.

• 1D, 2D techniques
• High dimensional data
• Tree-based techniques
• Network visualization
• Documents visualization

Visualization
Importance of colors Focus context
Interaction with visualization
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• 1D techniques

• Linear traverse of data

Table Lens Rao, 1994 ( Multivariate data )
Scatterplot Klein, 2002 ( Span Space )
LensBar ( InfoVis 1998 )
FacetMaps ( InfoVis 2006 )
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• 2D techniques

• Fit the 2nd dimension data to the first one, GIS
  applications

Large datasets Healey, 1999
Enridged contour maps van Wijk, Telea, Vis 2001
World mapper InfoVis 2006
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• nD techniques

• 2D restriction of screen
• Multiple views and projections

Scatterplot matrix Cleveland, 1985
Parallel coordinates Inselberg, 1990
generalization Moustafa, Wegman, 2002
Dimensional stacking Langton et al. 2007
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• nD techniques II.

• with help of user interaction

World within worlds Feiner, 1990
Hypercell Santos, 2002
Interactive scatterplots Kosara, 2004
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• nD techniques III.

• multiple views and projections for
  dimensionality reduction

Perspective wall Mackinlay et al. 1991
Prosection views Furnas, 1994
Sunflower Rose, 1999
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• Tree-based techniques

• data organized and explored via tree structure
• two different views of a tree
• Side view
• Top view

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• Tree-based techniques (side view)

• various forms of side view
• combined with user interaction to choose proper
  view

Cone tree Robertson et al., 1991
... generalized by Jeong Pang, 1998
Cylindrical tree Dachselt, Ebert, 2001
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• Tree-based techniques (top view)

• space filling problem

Tree map Shneiderman, 1992
Recent surveys on Tree maps http//www.cs.umd.edu
/hcil/treemap-history/index.shtml http//www.cse.o
hio-state.edu/kerwin/treemap-survey.html
800 files on disk
Cushion tree map Wijk, 1999
Ordered and quantum tree map Bederson, 2002
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• Tree-based techniques (top view)

Bar tree
Arc diagram
Analysis of state transition graphs Pretorius,
TVCG 2006
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• Visualizing network structure

• intended to visualize a structure of computer
  network
• a lot of items that need to be shown in a
  meaningful way
• closely related to graph drawing problem

H3 Directed graph in 3D hyperbolic space Munzer,
obertson et al., 1991
( video H3 )
MBone Munzer, 1996
Radial layout Yee, 2001
Edge bundles Holten, 2006
Topographic vis. Cortese, 2006
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• Document visualization

So much has already been written about
everything that cant find out anything about
it.
- James Thurber ( 1961 )

• Document visualization is not information
  retrieval
• Vast document storage www, digital libraries
  (structured vs. unstructured documents)
• Purpose to gain insight into content of text
  and text collections
• Emerged at the beginning of 90 with growing
  size of electronic text documents

Seesoft Eick, 1992
Tilebar Hearst, 1995
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• Document visualization

• growing size of documents vs. multidimensional
  browsing
• (Wise, 1995 Visualizing non-visual)

Spire Wise, 1995
In-Spire Pacific Northwest National
Lab. http//in-spire.pnl.gov 2004
( ThemeView )
( Starlight )

• ( Theme river )
• for temporal patterns

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• Summary of the first part

1D techniques
2D techniques
nD techniques
Table Lens Scatterplots LensBar FacetMaps
Maps with bars Enridged contour
maps Worldmapper
Scatterplot matrix Parallel coords. Dimensional
stacking
Tree-based techniques
Network visualization
Document visualization
H3 Edge bundles MBone
Linear nD techniques
Side-view Top-view
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• Focus context

• highlighted important parts of data
• put important into the context of the rest of
  data

Fisheye lens Furnas, 1981
Depth of field
also in scientific visualization Kruger, 2006

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• Visual attention

• Emphasizing important information
• ( by color, texture, depth of field )
• Cognitive psychology
• ( perception, long term vs. short term memory )

Kosara, S-DOF, 2002, 2003
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• Application Software visualization

• visualizing structure of software modules

Program structure Telea, 2002
Dynamic memory allocation Moreta, 2006
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• Application Material properties

• visualizing mechanical properties of materials
  (ZCU Plzen)
• attempt to visualize many information within a
  single picture

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• Summary conclusion

• Overview of the former and current state of
  Information visualization was presented
• 5 main areas of research (and many derived and
  combined)
• 1D techniques
• 2D techniques
• nD techniques
• Tree and graph-based visualization
• Network structure visualization
• Focus context paradigm
• Real-life application software visualization
• Two future directions
• Perception and cognition studies
• Large and dynamic data visualization

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• Thank you

• Actual papers and references used in this
  presentation can be found
• in the supplementary material distributed with
  this presentation.
• This work has been supported by the project 3DTV
  NoE FP6 No 511568
• and Ministry of Education, Youth and Sports of
  the Czech Republic
• project VIRTUAL No 2C06002.

Slavomir Petrik, Vaclav Skala Center of Computer
Graphics and Visualization http//herakles.zcu.cz
University of West Bohemia Plzen, Czech
Republic, 2007