Introduction to Information Visualization Slavomir Petrik, Vaclav Skala Centre of Computer Graphics and Visualization University of West Bohemia Plzen, Czech Republic 2007

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 century Roman Britain 15th century Leonardo da Vinci 1864 Civil war Today… Network structure 3 / 25

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 4 / 25

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 5 / 25

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 6 / 25

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 7 / 25

1D techniques Linear traverse of data Table Lens Scatterplot LensBar Rao, 1994 ( Multivariate data ) Scatterplot Klein, 2002 ( Span Space ) LensBar ( InfoVis 1998 ) FacetMaps ( InfoVis 2006 ) 8 / 25

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 9 / 25

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 10 / 25

nD techniques II. with help of user interaction World within worlds Feiner, 1990 Hypercell Santos, 2002 Interactive scatterplots Kosara, 2004 11 / 25

nD techniques III. multiple views and projections for dimensionality reduction Perspective wall Mackinlay et al. 1991 Prosection views Furnas, 1994 Sunflower Rose, 1999 12 / 25

Tree-based techniques data organized and explored via tree structure two different views of a tree Side view Top view 13 / 25

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 14 / 25

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.ohio-state.edu/~kerwin/treemap-survey.html 800 files on disk Cushion tree map Wijk, 1999 Ordered and quantum tree map Bederson, 2002 15 / 25

Tree-based techniques (top view) Bar tree + Arc diagram Analysis of state transition graphs Pretorius, TVCG 2006 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 space Munzer, obertson et al., 1991 ( video H3 ) MBone Munzer, 1996 Radial layout Yee, 2001 Edge bundles Holten, 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 Seesoft Eick, 1992 Tilebar Hearst, 1995 18 / 25

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 19 / 25

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 Side-view Top-view H3 Edge bundles MBone Linear nD 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 22 / 25

Application: Software visualization visualizing structure of software modules Program structure Telea, 2002 Dynamic memory allocation Moreta, 2006 23 / 25

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