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Introduction to Information Visualization
Slavomir Petrik, Vaclav SkalaCentre of Computer Graphics and VisualizationUniversity of West BohemiaPlzen, Czech Republic2007
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
2 / 25
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 centuryRoman Britain
1864Civil war
15th centuryLeonardo da
Vinci
Today…Network structure
3 / 25
Science of visualization
• Visualization of science vs. science of visualization
Large data01010101001000100011110010
01001 00100001111001000100
00100011 111001 1000100110
… .
Direct visualization vs.
visualization of structure
Scientific visualization
Information visualization
4 / 25
Areas of interest
Scientific visualizationdeals with direct visualization of data that have natural geometricstructure
Information visualizationdeals with more abstract data represented by trees or graphs
Visual Analyticsscientific investigation of the use of visualization in sense-making and reasoning
• Still not defined precisely !
5 / 25
Information visualization
Dataacquisition
Preprocessingenrichment, transformation
Basic concept
Examples
Ptolemy world map, 150 AD Napoleon march into RussiaCharles Minard, 1861
Data description by structures
Visualization
Highlight selected information
Information visualization
6 / 25
Information visualization II.
• 1D, 2D techniques• High dimensional data• Tree-based techniques• Network visualization• Documents visualization
Visualization
Importance of colorsFocus + context
Interaction with visualization
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1D techniques
• Linear traverse of data
Table LensRao, 1994 ( Multivariate data )
ScatterplotKlein, 2002 ( Span Space )
FacetMaps ( InfoVis 2006 )
LensBar ( InfoVis 1998 )
8 / 25
2D techniques
• Fit the 2nd dimension data to the first one, GIS applications
Large datasetsHealey, 1999
Enridged contour mapsvan Wijk, Telea, Vis 2001
World mapperInfoVis 2006
9 / 25
nD techniques
• 2D restriction of screen• Multiple views and projections
Scatterplot matrixCleveland, 1985
Parallel coordinatesInselberg, 1990
Dimensional stackingLangton et al. 2007
… generalization:Moustafa, Wegman, 2002
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nD techniques II.
• with help of user interaction
HypercellSantos, 2002
World within worldsFeiner, 1990
Interactive scatterplotsKosara, 2004
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nD techniques III.
• multiple views and projections for dimensionality reduction
Perspective wallMackinlay et al. 1991
Prosection viewsFurnas, 1994
SunflowerRose, 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 treeRobertson et al., 1991
... generalized by Jeong & Pang, 1998
Cylindrical treeDachselt, Ebert, 2001
14 / 25
Tree-based techniques (top view)
• space filling problem
Tree mapShneiderman, 1992
800 files on disk
Recent surveys on Tree maps:http://www.cs.umd.edu/hcil/treemap-history/index.shtmlhttp://www.cse.ohio-state.edu/~kerwin/treemap-survey.html
Cushion tree mapWijk, 1999
Ordered and quantum tree mapBederson, 2002
15 / 25
Tree-based techniques (top view)
Analysis of state transition graphsPretorius, TVCG 2006
+ Arc diagram
Bar tree
16 / 25
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 spaceMunzer, obertson et al., 1991
( video H3 )
MBoneMunzer, 1996
Radial layoutYee, 2001
Edge bundlesHolten, 2006
Topographic vis. Cortese, 2006
17 / 25
Document visualization
“So much has already been written about everything that can’t 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
TilebarHearst, 1995
SeesoftEick, 1992
18 / 25
Document visualization
• growing size of documents vs. multidimensional browsing
(Wise, 1995: Visualizing non-visual)
SpireWise, 1995
In-SpirePacific Northwest National Lab.http://in-spire.pnl.gov2004
( ThemeView )
( Starlight )
( Theme river )• for temporal patterns
19 / 25
Summary of the first part
1D techniques
Table LensScatterplots
LensBarFacetMaps
2D techniquesMaps with bars
Enridged contour maps
Worldmapper
nD techniques
Scatterplot matrix
Parallel coords.
Dimensional stacking
Tree-based techniques
Side-viewTop-view
Network visualizationH3
Edge bundlesMBone
Documentvisualization
LinearnD techniques
20 / 25
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 ]
21 / 25
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
Program structureTelea, 2002
Dynamic memory allocationMoreta, 2006
• visualizing structure of software modules
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Application: Material properties
• visualizing mechanical properties of materials (ZCU Plzen)
• attempt to visualize many information within a single picture
24 / 25
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
25 / 25
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 SkalaCenter of Computer Graphics and Visualizationhttp://herakles.zcu.cz
University of West BohemiaPlzen, Czech Republic, 2007