A Framework for Visualizing Information

160 Pages·2002·7.209 MB·English

by  Ed H. Chi (auth.)

A FRAMEWORK FOR VISUALIZING INFORMATION
THE KLUWER INTERNATIONAL SERIES ON HCI
VOLUME 1
Editors-in-Chief
John Karat, IBM Thomas Watson Research Center (USA)
Jean Vanderdonckt, Universite catholique de Louvain (Belgium)
Editorial Board
Gregory Abowd, Georgia Institute ofTechnology (USA)
Gaelle Calvary, IIHM-CLIPS-IMAG (France)
John Carroll, Virginia Institute ofTechnology (USA)
Gilbert Cockton, University of Sunderland ( United Kingdom)
Mary Czerwinski, Microsoft Research (USA)
Steve Feiner, Columbia University (USA)
Elizabeth Furtado, University of Fortaleza (Brazil)
Robert Jacob, Tufts University (USA)
Peter Johnson, University of Bath ( United Kingdom)
Robin Jeffries, SUN Microsystems (USA)
Philippe Palanque, Universire Paul Sabatier (France)
Oscar Pastor, Universidad Politecnica de Valencia (Spain)
Fabio Patemo, CNUCE-CNR (ltaly)
Chris Schmandt, Massachussets Institute ofTechnology (USA)
Markus Stolze, IBM Zürich (Switzerland)
Gerd Szwillus, Universität Paderborn (Germany)
Manfred Tscheligi, Center for Usability Research and Engineering (Austria)
Gerrit van der Veer, Vrije Universiteit Amsterdam (The Netherlands)
Shurnin Zhai, IBM Almaden Research Center (USA)
A Framework
for Visualizing Information
by
Ed H. Chi
Xerox Palo Alto Research Center,
Palo Alto, California, U.S.A.
SPRINGER-SCIENCE+BUSINESS MEDIA, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress
ISBN 978-90-481-6009-9 ISBN 978-94-017-0573-8 (eBook)
DOI 10.1007/978-94-017-0573-8
Printed on acid-free paper
All Rights Reserved
© 2002 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 2002
Softcoverreprint ofthe bardeover 1st edition 2002
No part of this work may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, electronic, mechanical, photocopying, microfilming, recording
or otherwise, without written permission from the Publisher, with the exception
of any material supplied specifically for the purpose of being entered
and executed on a computer system, for exclusive use by the purchaser of the work.
To every blade ofg rass I've brushed,
To every tree l've touched,
To every water way l've crossed,
To every mountain l've climbed,
To every photograph and step l've taken on
this journey
on this amazing planet.
Contents
List of Figures x1
List of Tables xm
Declaration xv
Preface xvii
Acknowledgments XIX
Foreword xxi
Abstract xxiii
1. INTRODUCTION 1
1 Motivation 1
2 Cognitive Advantages of the Information Visualization 4
2.1 Visual Information Processing 4
2.2 Sensemaking Cycle and Information Ecology 5
2.3 Levels of Knowledge 6
2.4 System Requirements of Visual Sensemaking 7
3 Data State Model and the Visualization Spreadsheet Supports
Visual Sensemaking 8
4 Visualization Spreadsheet Defined 9
5 Overview 10
2. DATA STATE REFERENCE MODEL 13
1 The Need for a Reference Model for Operations 14
1.1 Problems from End-Users' Perspective 14
1.2 Problems from Designers' Perspective 16
1.2.1 Providing a User Model 16
1.2.2 Extensibility to New Data Domains 17
1.3 Reference Model Helps Usersand Designers 18
Vll
viii A FRAMEWORK FOR VISUAL/ZING INFORMATION
2 Fundamental Properties of Operators 19
2.1 View versus Va1ue 19
2.2 Operational versus Functional Similarity 20
3 A Reference Model for Visualization Operators 21
3.1 Visualization Pipeline 21
3.2 Data State Model 24
3.3 Example: Web Analysis in the Data State Model 26
4 Classification of Operators using the Framework 29
4.1 Example: Web Analysis Visualization Operators 31
5 Properties of the Framework 32
5.1 View versus Value 32
5.2 Applicability of Operators 34
5.3 Operator-Centric Approach 36
5.4 Direct Manipulation 37
5.5 Implementation Choices 38
6 Discussion 39
6.1 Three Classes of Usersofthis Model 39
6.2 End-User Advantages using this Framework 39
7 Summary 40
3. VALIDATION OF MODEL 43
4. EXPRESSIVENESS OF DATA STATE MODEL 51
1 Expanding the Data Flow Model 52
2 Visualization Equivalence 53
3 Analysis of Characteristics 57
3.1 Data Flow Modeland Data Flow Visualization Systems
Analysis 58
3.2 Data State Model Analysis 60
3.3 Discussion 61
4 Summary 62
5. VISUALIZATION SPREADSHEET ILLUSTRATED 65
1 Research Vision 66
2 Research Approach 68
3 Original Data Domain: Genetic Sequence Similarity 68
4 Other Data Domains 74
4.1 Time-series Matrices 75
4.2 Algorithm Visualization 76
Contents ix
5 Illustrated Principles 76
5.1 Derive Comparison Data Sets 76
5.2 Apply Operators in Parallel 83
5.3 Extract Multiple Visual Features Simultaneously 86
5.4 Create Analysis Templates 89
5.5 Update Automatically via Dependency Links 90
5.6 Mapping Value to Structure using Custom Layouts 92
5.7 Use Both Direct Manipulation and Textual Commands 93
6 Summary 96
6. DETAILED CASE STUDY: WEB ANALYSIS VISUALIZATION
SPREADSHEET 99
Visualization of Web Space 99
2 Real-World Analysis Scenarios 100
3 Summary 107
7. IMPLEMENTATION EXPERIENCE 109
1 Prototype: Spreadsheet for Similarity Reports 110
2 Spreadsheet for Information Visualization Implementation 112
3 Discussion 115
4 Lessons Learned: Answers to High-Level Challenges 117
5 Summary 119
8. RELATED WORK 121
Reference Model for Information Visualization 121
2 Spreadsheet 124
3 Summary 129
9. CONCLUSION 131
References 137
Index
145
List of Figures
1.1 Visual Sensemaking Cycle 5
2.1 The Information Visualization Reference Model 23
2.2 Our visualization operator framework: Data State Model 25
2.3 The Delaunay Triangulation Algorithm Visualization Pipeline 27
2.4 The Web Analysis Visualization Pipeline 28
2.5 Multiple Ievel of semantics for the addition operator. 35
4.1 An example of the Duality Transformation 56
4.2 An example of a AVS Data Flow network that enables
users to specify the visualization process of visualizing
a molecule. Each operator is succinctly specified graphically. 59
5.1 Grand Vision for Visualization Spreadsheet research 67
5.2 Research Plan for Spreadsheet for Visualization system. 69
5.3 Molecular biology seeks to determine the interaction
between gene, protein, protein structure, and protein
function. Similarity algorithms provide a shortcut for
finding possible protein functions for an unknown sequence. 70
5.4 Several alignments represented in AlignmentViewer:
X-axis is the position along the input sequence, Y-axis
is the similarity score, and the Z-axis is the frame num-
ber of the alignment. 72
5.5 Visualization of 3D random point generation and De-
launay triangulation of the resulting point set. 78
XI
xii A FRAMEWORK FOR VISUALIZING INFORMATION
5.6 Generating cells 4_1, 4_2, and 4_3 in the Delaunay Tri-
angulation example. Visualizing intermediate steps and
then using addition to construct the final visualization
in the understanding of 3D Delaunay Triangulation al-
gorithm. 79
5.7 A screen snapshot of the Spreadsheet for Similarity Re-
ports visualization system. 81
5.8 An example of the parallel application of direct manip-
ulation operations to multiple cells simultaneously. 84
5.9 Visualization of time-series matrices using the Spread-
sheet for Information Visualization system. 85
5.10 Discovering novel patterns using multiple visualization
representations in the time-series matrices example. 88
5.11 The Delaunay Triangulation Algorithm Visualization
Dependency Flow Chart 91
5.12 Drop-down menus makes Spreadsheet for Similarity
Reports (SSR) easy to use. 94
5.13 Commands and scripts in the SIV spreadsheet 95
6.1 Web Analysis Visualization Spreadsheet showing the
Xerox.com Web site using Cone Trees. 102
6.2 Faddish of Information in the Xerox.com Web site. 103
6.3 Web Analysis Visualization Spreadsheet with Disk Trees. 103
6.4 Creation of new Web content for Product Farnilies in
Xerox.com. 104
6.5 Visual usage pattem subtraction shows differences in
usage quickly. 106
6.6 Straight mapping of usage pattem does not show dif-
ference in usage at all. 107
6.7 Spreading Activation visualization enable visualization
of related contents using document sirnilarity. 107
7.1 Interactions and Control Flow of the Spreadsheet for
Information Visualization System 113
7.2 Architecture of the Spreadsheet for Information Visu-
alization System 114
8.1 Chuah and Roth's Basic Visualization Interaction taxonomy 123
9.1 Stages of the Visual Sensemaking Cycle 134