Introduction to Information Visualization ... - Semantic Scholar

Introduction to Information Visualization supplementary material Slavomir Petrik∗ University of West Bohemia

Vaclav Skala† University of West Bohemia

Figure 1: This material provides insight into various aspects and areas of Information visualization and server as a supplementary material to the main presentation. A brief history is sketched, followed by the specific areas of research. Finally, conclusion and possible future directions of research are discussed together with references to the related papers and books.

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Information Visualization

History of effort to store and visualize some information begin deep in the times when Babylon has been considered as the middle of known world. Since then many documents preserve, helping to create compact picture of the Information visualization (hereafter InfoVis) evolution. With the steep growth of data size in the second half of 20th century one question began to be more and more important: ”What is more efficient for data understanding? To visualize data directly or show their structure and features of interest?” This was the point at which data visualization have begun to recognize between Scientific visualization and Information visualization. While the first one focuses of direct visualization of data with natural geometric structure (e.g. volumetric data), the latter deals with more abstract representation of data like trees or graphs. This introduction is divided into two parts. The first part deals with 1D, 2D, nD techniques, tree-based techniques and network and document visualization. The second part introduces Focus + context principle and tools of visual attention. 1D techniques 1D techniques visualize data (possibly of high dimensionality) through their linear view. Rao et al. introduce Table Lens [Rao and Card 1994]. Another solution has been found in Scatterplots, visualizing data as a point in 2D or 3D space. Each single axis in a Scatterplot represents different quantity of interest. LensBar technique has been proposed by Masui [Masui 1998] to browse large lists of items. Recently the Facet Map [Smith et al. 2006] technique ∗ e-mail: † e-mail:

[email protected] [email protected]

has been introduced by Smith et al. Facet maps provides efficient searching among data items when search target is not exactly clear but can be identified by refining initial wide search query. 2D techniques 2D techniques use overlapping of multiple scalar field to enrich the information value of visualization. Healey [Healey and Enns 1999] used 3D glyphs over 2D geographical map to show values of observed quantity over at various geographical locations. Vijk and Telea introduced Enridged contour maps. In 2006 the Worldmapper technique [Dorling et al. 2006] introduced visualization of various statistical values at different world location by properly deforming the shape of countries on the map of world. nD techniques Most of the techniques for visualization of n-dimensional data tries to reduce dimensionality of the data in order to by able to visualize them in with 2D images. Techniques like Parallel coordinates [Inselberg and Dimsdale 1990; Moustafa and Wegman 2002] or Dimensional Stacking [Langton et al. 2007] tries to show multiple dimensions such that axis are non-parallel. Other group of techniques for n-dimensional data uses user interaction to select a proper view to show only certain number of dimensions and their relations [Feiner and Beshers 1990; dos Santos and Brodlie 2002; Kosara et al. 2004]. Finally, multiple projections and views are used to reduce dimensionality in tools like Persepctive wall [Mackinlay et al. 1991], Prosection views [Furnas and Buja 1994] or Sunflower [Rose 1999]. Tree-based visualization Tree-based techniques use two different ways of tree visualization. Side view techniques show a tree as known from the nature [Robertson et al. 1991; Jeong and Pang 1998; Dachselt and Ebert 2001; Pretorius and Wijk 2006]. Top view techniques solve the space filling problem to show various levels of tree in a 2D plane [Shneiderman 1992; van Wijk and van de Wetering 1999; Bederson et al. 2002]. Network visualization Graph-based techniques for network visualization deals mainly with problem of huge amount of items within a single image. The

curves connecting various geographical location on the globe were used to visualize structure of MBone network [Munzner 1996]. Later, projection of items from hyperbolic space onto sphere was used by Munzer [Munzner 1997]. Recently two new techniques were introduced: Edge bundles [Holten 2006] and Topographic visualization by Cortese et al. [Cortese et al. 2006]. Document visualization Area of document visualization focuses of visualizing results of search queries within text document or collection of documents. Examples could be system Seesoft [Eick et al. 1992] and Tilebar technique [Hearst 1995]. Problem of visualizing structure of multimodule program has been addressed by Telea et al. [Telea et al. 2002]. With growing amount of textual information, visualization of its structure moved into more dimensions (e.g. SPIRE [Wise et al. 1995] or IN-SPIRE system developed at Pacific Northwest National Laboratories or). Focus + context Focus + context is one of the basic principle used in Information visualization. The goal is to emphasize the important information and put the highlighted part of information into the context with the rest of data. Applications of Focus + context principle have many different forms like: Fisheye lens [Furnas 1981], varying depth of field [Kosara et al. 2002] or proper volume rendering in Scientific visualization [Krger et al. 2006].

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Directions for future research

As the amount of data constantly grows in the past decades, the techniques for visualization of their structure must be more and more effective. This effectiveness has two important aspects. Firstly, effectiveness in the terms of space and time complexity of the methods. Secondly, visual representation of the proposed structure must provide convenient way to browse large data. Another challenge are new ways to visualize temporal behavior of the data [Moreta and Telea 2007]. Acknowledgements 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. We also thank to our colleagues which contributed to this work by their comments and consultations.

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S ANTOS , S. R., AND B RODLIE , K. W. 2002. Visualizing and investigating multidimensional functions. In VISSYM ’02: Proceedings of the symposium on Data Visualisation 2002, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, 173–ff.

DOS

E ICK , S., S TEFFEN , J., AND J R ., E. S. 1992. Seesoft - a tool for visualizing line oriented software statistics. IEEE Transactions on Software Engineering 18, 11, 957–968. F EINER , S. K., AND B ESHERS , C. 1990. Worlds within worlds: metaphors for exploring n-dimensional virtual worlds. In Proc. of the 3rd annual ACM SIGGRAPH symposium on User interface software and technology, ACM Press, New York, NY, USA, 76–83. F URNAS , G. W., AND B UJA , A. 1994. Prosection views: dimensional inference through sections and projections. with a discussion by John F. Elder IV, Shingo Oue and Daniel B. Carr and a rejoinder by the authors. Journal of Computational and Graphical Statistics 3, 4, 323–385. F URNAS , G. W. 1981. The FISHEYE view: A new look at structured files. Tech. Rep. #81-11221-9, Murray Hill, New Jersey 07974, U.S.A., 12 . H EALEY, C. G., AND E NNS , J. T. 1999. Large datasets at a glance: Combining textures and colors in scientific visualization. IEEE Transactions on Visualization and Computer Graphics 5, 2 (/), 145–167. H EARST, M. A. 1995. Tilebars: Visualization of term distribution information in full text information access. In Proc. of the Conference on Human Factors in Computing Systems, CHI’95. H OLTEN , D. 2006. Hierarchical edge bundles: Visualization of adjacency relations in hierarchical data. IEEE Transactions on Visualization and Computer Graphics 12, 5, 741–748. I NSELBERG , A., AND D IMSDALE , B. 1990. Parallel coordinates: a tool for visualizing multi-dimensional geometry. In Proc. of the 1st conference on Visualization ’90, IEEE Computer Society Press, Los Alamitos, CA, USA, 361–378. J EONG , C.-S., AND PANG , A. 1998. Reconfigurable disc trees for visualizing large hierarchical information space. In Proc. of the 1998 IEEE Symposium on Information Visualization, IEEE Computer Society, Washington, DC, USA, 19–25. K LEIN , P., M LLER , H., R EITERER , F., AND E IBL , H., 2002. Visual information retrieval with the supertable + scatterplot. KOSARA , R., M IKSCH , S., H AUSER , H., S CHRAMMEL , J., G ILLER , V., AND T SCHELIGI , M. 2002. Useful properties of semantic depth of field for better f+c visualization. In Proc. of the Joint Eurographics–IEEE TCVG Symposium on Visualization 2002, 205–210.

DACHSELT, R., AND E BERT, J. 2001. Collapsible cylindrical trees: A fast hierarchical navigation technique. In IEEE Symposium on Information Visualization 2001 (INFOVIS’01), 79–86.

KOSARA , R., S AHLING , G., AND H AUSER , H., 2004. Linking scientific and information visualization with interactive 3d scatterplots.

D ORLING , D., BARFORD , A., AND N EWMAN , M. 2006. Worldmapper: The world as you’ve never seen it before. IEEE Transactions on Visualization and Computer Graphics 12, 5, 757–764.

K RGER , J., S CHNEIDER , J., AND W ESTERMANN , R. 2006. Clearview: An interactive context preserving hotspot visualization technique. IEEE Transactions on Visualization and Computer Graphics 12, 5, 941–948.

L ANGTON , J. T., P RINZ , A. A., AND H ICKEY, T. J. 2007. Neurovis: combining dimensional stacking and pixelization to visually explore, analyze, and mine multidimensional multivariate data. In Proc. of SPIE: Visualization and Data Analysis (VDA 2007), vol. 6495. M ACKINLAY, J. D., ROBERTSON , G. G., AND C ARD , S. K. 1991. The perspective wall: detail and context smoothly integrated. In CHI ’91: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM Press, New York, NY, USA, 173–176. M ASUI , T. 1998. Lensbar - visualization for browsing and filtering large lists of data. In Proc. IEEE Symposium on Information Visualization 1998, 113–120. M ORETA , S., AND T ELEA , A. 2007. Visualizing dynamic memory allocations. In Proc. of VISSOFT 2007, 31–38. M OUSTAFA , R. E., AND W EGMAN , E. J. 2002. On some generalization of parallel coordinate plots. Tech. rep., George Mason University, Fairfax, VA, USA. M UNZNER , T. 1996. Visualizing the global topology of the mbone. In Proceedings of the 1996 IEEE Symposium on Information Visualization, 85–92. M UNZNER , T. 1997. H3: Laying out large directed graphs in 3d hyperbolic space. In Proc. of the 1997 IEEE Symposium on Information Visualization, 2–10. P RETORIUS , A. J., AND W IJK , J. J. V. 2006. Visual analysis of multivariate state transition graphs. IEEE Transactions on Visualization and Computer Graphics 12, 5, 685–692. R AO , R., AND C ARD , S. K. 1994. The table lens: Merging graphical and symbolic representations in an interactive focus context visualization for tabular information. In Proc. ACM Conf. Human Factors in Computing Systems, CHI, ACM. ROBERTSON , G. G., M ACKINLAY, J. D., AND C ARD , S. K. 1991. Cone trees: animated 3d visualizations of hierarchical information. In CHI ’91: Proceedings of the SIGCHI conference on Human factors in computing systems, ACM Press, New York, NY, USA, 189–194. ROSE , S. J. 1999. The sunflower visual metaphor, a new paradigm for dimensional compression. infovis 00, 128. S HNEIDERMAN , B. 1992. Tree visualization with tree-maps: 2-d space-filling approach. ACM Trans. Graph. 11, 1, 92–99. S MITH , G., C ZERWINSKI , M., M EYERS , B., ROBBINS , D., ROBERTSON , G., AND TAN , D. S. 2006. Facetmap: A scalable search and browse visualization. IEEE Transactions on Visualization and Computer Graphics 12, 5, 797–804. T ELEA , A., M ACCARI , A., AND R IVA , C. 2002. An open visualization toolkit for reverse architecting. In IWPC ’02: Proceedings of the 10th International Workshop on Program Comprehension. W IJK , J. J., AND T ELEA , A. 2001. Enridged contour maps. In Proc. of the conference on Visualization ’01, IEEE Computer Society, Washington, DC, USA, 69–74.

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W ISE , J. A., T HOMAS , J. J., P ENNOCK , K., L ANTRIP, D., P OTTIER , M., S CHUR , A., AND C ROW, V. 1995. Visualizing the non-visual: spatial analysis and interaction with information from text documents. In INFOVIS ’95: Proceedings of the 1995 IEEE Symposium on Information Visualization, 51.

Overview of the talk

Introduction to Information Visualization

•

History of visualization

•

Scientific visualization vs. Information visualization

•

Concepts, directions and techniques of InfoVis • • •

Slavomir Petrik, Vaclav Skala

1D, 2D, nD techniques Tree and graph-based vis. Network structure vis.

•

Visualization in InfoVis

•

Interacting with visualization

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

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From single sketch to tree maps

Science of visualization

World map with Babylon in its centre 2300 BC (British museum)

• Visualization of science vs. science of visualization Scientific visualization

Large data

Growing amount of information within a single image …

01010101001000100011110010 01001 00100001111001000100

Direct visualization vs. visualization of structure

00100011 111001 1000100110 … .

14th century Roman Britain

15th century Leonardo da Vinci

1864 Civil war

Information visualization

Today… Network structure 3 / 25

4 / 25

Areas of interest

Information visualization

• Still not defined precisely !

Examples

Scientific visualization deals with direct visualization of data that have natural geometric structure

Information visualization

Ptolemy world map, 150 AD

deals with more abstract data represented by trees or graphs

Napoleon march into Russia Charles Minard, 1861

Basic concept

Visual Analytics scientific investigation of the use of visualization in sense-making and reasoning

Information visualization

Data acquisition

5 / 25

Preprocessing enrichment, transformation

Data description by structures

Visualization

Highlight selected information

6 / 25

1

Information visualization II.

1D techniques • Linear traverse of data

• • • • •

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

Table Lens

Visualization

Rao, 1994 ( Multivariate data )

Importance of colors Focus + context

Scatterplot Klein, 2002 ( Span Space )

LensBar ( InfoVis 1998 )

Interaction with visualization

FacetMaps ( InfoVis 2006 )

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2D techniques • Fit the

2nd

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nD techniques

dimension data to the first one, GIS applications

• 2D restriction of screen • Multiple views and projections

Large datasets Healey, 1999

Scatterplot matrix Cleveland, 1985

Enridged contour maps van Wijk, Telea, Vis 2001

Parallel coordinates Inselberg, 1990 … generalization: Moustafa, Wegman, 2002

Dimensional stacking

World mapper

Langton et al. 2007

InfoVis 2006

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nD techniques II.

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nD techniques III.

• with help of user interaction

• multiple views and projections for dimensionality reduction Perspective wall

World within worlds

Mackinlay et al. 1991

Feiner, 1990

Hypercell

Prosection views

Santos, 2002

Furnas, 1994

Sunflower Rose, 1999

Interactive scatterplots Kosara, 2004 11 / 25

12 / 25

2

Tree-based techniques

Tree-based techniques (side view)

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

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

• Side view

Cone tree Robertson et al., 1991 ... generalized by Jeong & Pang, 1998

• Top view

Cylindrical tree Dachselt, Ebert, 2001 14 / 25

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Tree-based techniques (top view)

Tree-based techniques (top view) • space filling problem Tree map

Bar tree

Shneiderman, 1992

+ Arc diagram 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

Analysis of state transition graphs

800 files on disk

Cushion tree map

Ordered and quantum tree map

Wijk, 1999

Bederson, 2002

Pretorius, TVCG 2006

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Visualizing network structure

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Document visualization “So much has already been written about everything that can’t find out anything about it.”

• 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

- James Thurber ( 1961 )

H3 Directed graph in 3D hyperbolic space Munzer, obertson et al., 1991

• 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

( video H3 )

MBone

Radial layout

Edge bundles

Topographic vis.

Munzer, 1996

Yee, 2001

Holten, 2006

Cortese, 2006

Seesoft Eick, 1992 17 / 25

Tilebar Hearst, 1995 18 / 25

3

Document visualization

Summary of the first part

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

Spire Wise, 1995

In-Spire

1D techniques

2D techniques

nD techniques

Table Lens Scatterplots LensBar FacetMaps

Maps with bars

Scatterplot matrix

Enridged contour maps

Parallel coords.

Pacific Northwest National Lab. http://in-spire.pnl.gov 2004

Worldmapper

Dimensional stacking

( ThemeView ) ( Starlight ) ( Theme river ) • for temporal patterns

Tree-based techniques Side-view Top-view

Network visualization

Document visualization

H3 Edge bundles

Linear nD techniques

MBone

19 / 25

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Focus & context

Visual attention

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

• Emphasizing important information ( by color, texture, depth of field ) Kosara, S-DOF, 2002, 2003

Fisheye lens [ Furnas, 1981 ]

• Cognitive psychology

Depth of field

( perception, long term vs. short term memory )

… also in scientific visualization [ Kruger, 2006 ] 21 / 25

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Application: Software visualization

Application: Material properties

• visualizing structure of software modules

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

Program structure Telea, 2002

Dynamic memory allocation Moreta, 2006 23 / 25

24 / 25

4

Summary & conclusion

Thank you

• Overview of the former and current state of Information visualization was presented • 5 main areas of research (and many derived and combined) Actual papers and references used in this presentation can be found

• 1D techniques • 2D techniques

in the supplementary material distributed with this presentation.

• nD techniques • Tree and graph-based visualization • Network structure visualization

This work has been supported by the project 3DTV NoE FP6 No: 511568

• Focus & context paradigm

and Ministry of Education, Youth and Sports of the Czech Republic

• Real-life application: software visualization

project VIRTUAL No: 2C06002.

Two future directions: • Perception and cognition studies

Slavomir Petrik, Vaclav Skala Center of Computer Graphics and Visualization http://herakles.zcu.cz

• Large and dynamic data visualization

University of West Bohemia Plzen, Czech Republic, 2007

25 / 25

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