Data Visualization 2000: Proceedings of the Joint EUROGRAPHICS and IEEE TCVG Symposium on Visualization in Amsterdam, The Netherlands, May 29–30, 2000

296 Pages·2000·1.28 MB·English

by  G. Melançon

W. de Leeuw
R. van Liere (eds.)
Data Visualization 2000
Proceedings of the Joint
EUROGRAPHICS and IEEE TCVG
Symposium on Visualization
in Amsterdam, The Netherlands,
May 29-31, 2000
Eurographics
SpringerW ienN ewYork
Dr. ir. Willem Comelis de Leeuw
ir. Robert van Liere
Center for Mathematics and Computer Science,
Amsterdam, The Netherlands
This work is subject to copyright.
All rights are reserved, whether the whole or part of the material is concerned, specifically those
of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying
machines or similar means, and storage in data banks.
© 2000 Springer-VerlaglWien
Reprint of the original edition 2000
Typesetting: Camera-ready by authors
Graphic design: Ecke Bonk
Printed on acid-free and chlorine-free bleached paper
SPIN: 10768503
With 166 partly coloured Figures
ISSN 0946-2767
ISBN-13: 978-3-211-83515-9 e-ISBN-13: 978-3-7091-6783-0
DOl: 10.1007/978-3-7091-6783-0
Preface
This book contains the papers presented at VisSym '00, the Second Joint Visu
alization Symposium organized by the Eurograp'hics Association and the IEEE
Computer Society Technical Committee on Visualization and Graphics (TCVG).
The event took place from May 28 to May 31, 2000, in Amsterdam. We hope
that these papers will be valuable, not only for visualization researchers, but
also for practitioners developing or using visualization applications.
We are glad to report that the visiblility of the symposium continues to in
crease and that visualization researchers and practitioners from allover the world
have submitted papers. This year, 66 papers and case studies were submitted
of which 27 were accepted. In addition, we are glad to see that the focus of the
symposium is also expanding. Topics are shifting from scientific data visualiza
tion (eg. flow and volume visualization) towards new areas in visualization. We
accepted 7 research papers on information visualization and there was a broad
range of other topics.
We would like to thank all those involved in organizing the symposium. In
particular, special thanks to Wim de Leeuw and Guy Melan!,;on who managed
the electronic paper submission and review process. Also, many thanks to Mieke
Brune who was in charge of the local organization. We want to thank the inter
national program committee for their excellent, yet laborious, job in reviewing
all submitted papers. The high quality of the symposium is a reflection of the
quality of the submitted papers and the quality of the reviewing process.
Symposium co-chairs:
Ivan Herman
William llibarsky
Robert van Liere
Paper co-chairs:
Frits Post
Jarke van Wijk
Chairs, IPC, and Reviewers
Symposium Co-Chairs
Robert van Liere, CWI, Amsterdam, Netherlands
Ivan Herman, CWI, Amsterdam, Netherlands
William Ribarsky, Georgia Institute of Technology, Atlanta, Georgia
Paper Co-Chairs
Frits Post, Delft Universiy of Technology, Netherlands
Jarke van Wijk, Technical University Eindhoven, Netherlands
Organizing Co-Chairs
Wim de Leeuw, CWI Amsterdam, Netherlands
Guy Melan~on, CWI Amsterdam, Netherlands
International Program Committee
D. Bartz, H. Hagen, H.-G. Pagendarm,
K. Brodlie, H. Hauser, H. Pfister,
S. Card, I. Herman, F. Post,
D. Cohen-Or, A. Kaufman, W. Ribarsky,
S. Coquillart, D. Keirn, M. Rumpf,
R. Crawfis, D. Kenwright, G. Sakas,
S. Eick, W. de Leeuw, R. Scopigno,
T. Ertl, R. van Liere, D. Silver,
S. Gibson, W. Lefer, P. Slavik,
M. Grave, N.Max, R. Spence,
E. GroUer, R. Moorhead, H. Spoelder,
M. Gross, G. Nielson, J. van Wijk
Additional reviewers
C. Bajaj, C. Johnson, G. Melan~on
G. Di Battista, M. van Kreveld, J. Mulder,
D. Bergeron, T. Kuipers, T. Munzner,
S. Gumhold, K.-L. Ma, A. Pang,
VIII
K. Polthier, J. Smit, R. Veltkamp,
A. Sadarjoen, P.J. Stappers, M. Ward,
D. Saupe, R. Tamassia, R. Westermann,
H. Schumann, L. Treinish, H. van de Wetering
Table of Contents
Invited Speaker. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Information Visualization
DAG Drawing from an Information Visualization Perspective. . . . . . . . . . . . 3
Guy Melan{:on, Ivan Herman
Contextual Visualization of Actor Status in Social Networks. . . . . . . . . . . .. 13
Ulrik Brandes, Dorothea Wagner
Improving Angular Resolution in Visualizations of Geographic Networks.. 23
Ulrik Brandes, GaUna Shubina, Roberto Tamassia
Squarified Treemaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 33
Mark Brnls, Kees Huizing, Jarke J. van Wijk
Dynamic Overview Techniques for Image Retrieval . . . . . . . . . . . . . . . . . . . .. 43
Pearl Pu, Zoran Pecenovic
Drawing Relational Schemas ........................................ 53
Giuseppe Di Battista, Walter Didimo, Maurizio Patrignani, and Mau-
rizio Pizzonia
Large Datasets and Multi Resolution
Hierarchical Data Representations Based on Planar Voronoi Diagrams. . .. 63
Shirley Schuss man, Martin Bertram, Bernd Hamann, Kenneth 1. Joy
Skeleton Graph Generation for Feature Shape Description. . . . . . . . . . . . . .. 73
F'reek Reinders, Melvin E.D. Jacobson, Frits H. Post
Progressive Volume Models for Rectilinear Data using Tetrahedral Coons
Volumes .......................................................... 83
David J. Holliday, Gregory M. Nielson
Hardware Accelerated Wavelet Transformations. . . . . . . . . . . . . . . . . . . . . . .. 93
Matthias Hopi, Thomas Ertl
Multiresolution Techniques for Interactive Texture-Based Rendering of Ar-
bitrarily Oriented Cutting Planes .................................... 105
Eric LaMar, Mark A. Duchaineau, Bernd Hamann, Kenneth 1. Joy
x
Very Large Scale Visualization Methods for Astrophysical Data .......... 115
Andrew J. Hanson, Chi-Wing Fu, Eric A. Wernert
Volume and Flow Visualization
Hybrid Model for Vascular Tree Structures ...................... , ..... 125
Anna Puig, Dani Tost and Isabel Navazo
Direct Volume Rendering from Photographic Data ............... " ..... 137
David Ebert, Tim McClanahan, Penny Rheingans, and Terry Yoo
Variational Approach to Vectorfield Decomposition ............... " ..... 147
Konrad Polthier and Eike PreufJ
Visualization Systems
Integrated Multiresolution Geometry and Texture Models for Terrain Vi-
sualization ........................................................ 157
Konstantin Baumann, Jurgen Dollner, Klaus Hinrichs
A Framework for Interactive Hardware Accelerated Remote 3D-Visualization 167
Klaus Engel, Ove Sommer, Thomas Ertl
Appearance-Based Virtual-View Generation for Fly Through in a Real
Dynamic Scene .................................................... 179
Shigeyuki Baba, Hideo Saito, Sundar Vedula, Kong Man Cheung, Takeo
Kanade
SmartLink: An Agent for Supporting Dataflow Application Construction .. 189
Alexandru Telea, Jarke J. van Wijk
Design of Visualizations for Urban Modeling .................... " ..... 199
L. Denise Pinnel, Matthew Dockrey, A.J. Bernheim Brush, Alan Born-
ing
ViSSh: A Data Visualisation Spreadsheet ....................... " ..... 209
Fabian Nunez, Edwin Blake
Applications and Case Studies
Fast Visualization of Special Relativistic Effects on Geometry and Illumi-
nation ............................................................ 219
Daniel Weiskopf
XI
AlVis - An Aluminium-Foam Visualization and Investigation Tool ........ 229
Andreas H. Konig, Helmut Doleisch, Andreas Kottar, Brigitte Kriszt,
Eduard Groller
WWW-based Visualization of the Real Time Run of a Space Weather
Forecasting Model ....... , ......................................... 239
Sergei Maurits, Jeff McAllister, Brenton Watkins
Towards visual matching as a way of transferring pre-operative surgery
planning .......................................................... 249
Stijn De Buck, Johan Van Cleynenbreugel, Guy Marchal, Paul Suetens
A Case Study of Isosurface Extraction Algorithm Performance ........... 259
Philip M. Sutton Charles D. Hansen Han-Wei Shen Dan Schikore
Case Study: Resource Steering in a Visualization System ................ 269
Ed H. Chi, John T. Riedl
Authors Index ................................................ 279
Color Plates ................................................... 281
DAG Drawing from an Information Visualization
Perspective
G. Melan~on and I. Herman
Centrum voor Wiskunde en Informatica (CWI)
P.O. Box 94079
1090 OB Amsterdam, The Netherlands
{Guy.Melancon,Ivan.Herman,}@cwi.nl
Abstract. When dealing with a graph, any visualization strategy must rely on a
layout procedure at least to initiate the process. Because the visualization process
evolves within an interactive environment the choice of this layout procedure is
critical and will often be based on efficiency.
This paper compares two popular layout strategies, one based on the extraction
of a spanning tree, the other based on edge crossing minimization of directed
acyclic graphs. The comparison is made based on a large number of experimen
tal evidence gathered through random graph generation. The main conclusion of
these experiments is that, contrary to the popular belief, usage of edge crossing
minimization algorithms may be extremely useful and advantageous, even under
the heavy requirements of information visualization.
1 Introduction
Graph visualization has emerged lately as a sub-field of information visualization, spe
cializing in visualizing data that comes with inherent relations. In that case, the data to
be visualized is interpreted as a graph, and applications usually offer different strategies
to view, to navigate in, and to interact with the graph. This has many areas of applica
tion in biology, chemistry, computer science, web navigation, or document management
systems. See [6].
Graph visualization can rely on the rich body of knowledge developed over the
years by the graph drawing community, which grew around the yearly Symposia on
Graph Drawing. A large number of layout strategies are at disposal, which have been
collected recently in the book of di Battista et at. [1]. However, it is not always easy to
decide which layout algorithm to use for a given application and for a specific class of
graphs: the practical requirements, concerns, assumptions, etc., are often different. The
graph drawing community usually assumes graphs to be sparse and to contain only a
small number of nodes. A few hundred nodes, containing only about two times more
edges than the number of nodes, is a common measure. In contrast, the information
visualization community has to deal with graphs often containing thousands of nodes.
Of course, large graphs are often clustered, yielding much smaller graphs; nevertheless,
the real size and density numbers are still different. This issue will be examined in more
details later in the paper.
W. C. de Leeuw et al. (eds.), Data Visualization 2000
© Springer-Verlag/Wien 2000