Expanding the Frontiers of Visual Analytics and Visualization (eBook)

Expanding the Frontiers of Visual Analytics and Visualization 3
Foreword 6
Preface 9
Contents 11
List of Contributors 14
Editors 14
Invited Authors (in alphabetical order) 16
Co-authors (in alphabetical order) 30
Chapter 1: Introduction-The Best Is Yet to Come 45
1.1 A Tribute 45
1.2 Background to Visual Analytics and Visualization 46
1.3 Resources for Visual Analytics and Visualization 47
1.4 International Conferences 48
1.5 This Volume 48
References 49
Part I: Evolving a Vision 50
Chapter 2: An Illuminated Path: The Impact of the Work of Jim Thomas 51
2.1 Introduction 51
2.2 Three Datasets 53
2.3 Illuminating the Path 55
2.3.1 The Spread of the Impact 55
2.3.2 The Inspired Community 57
2.3.3 A Document Co-citation Analysis 58
2.3.4 Major Co-citation Clusters 59
2.3.5 Landmark Papers 61
2.3.5.1 Citation Counts 61
2.3.5.2 Betweenness Centrality 61
2.3.5.3 Burst and Sigma 62
2.3.6 Timeline View 62
2.4 A Broader Context 63
2.4.1 The Trend of Growth 63
2.4.2 Major Source Journals and Hot Topics 64
2.4.3 Highly Cited Documents and Authors 65
2.4.4 Mapping the Visual Analytics Domain 65
2.4.5 An Overlay of Network D2 in Network D3 71
2.5 Conclusion 72
References 72
Chapter 3: The Evolving Leadership Path of Visual Analytics 73
3.1 Leadership Lifecycle 73
3.2 Mind the Gap 74
3.3 Bold Vision 76
3.4 Champions on Board 77
3.5 Structures and Collaborations 79
3.6 Technology Deployment 80
3.7 Strategies for Future Growth 81
3.7.1 Increase Domains and Applications 81
3.7.2 Better Integrate the Communities Within Visual Analytics 82
3.7.3 Broaden the Base of Support 82
3.8 The Path Ahead 83
References 84
Part II: Visual Analytics and Visualization 85
Chapter 4: Visual Search and Analysis in Complex Information Spaces-Approaches and Research Challenges 86
4.1 Introduction 87
4.2 De?nition of Complex Data Sets 88
4.3 Tasks and Problems of Visual Search and Analysis in Complex Data 90
4.3.1 Visual Search and Analysis 90
4.3.2 Problems in Presence of Complex Data 91
4.3.2.1 Visual Search 92
4.3.2.2 Visual Analysis 93
4.4 Approaches 94
4.4.1 Generic Examples for Visual Search and Analysis Systems 94
4.4.2 Example Approaches to Visual Search and Analysis of Type-Complex Data 95
4.4.2.1 Visual Search in 3D Object Data 95
4.4.2.2 Visual Search in Graphs-Visual Query De?nition 96
4.4.2.3 Visual Search and Analysis of Biochemical Data-Similarity Function De?nition Using Visual Comparison of Descriptors 98
4.4.3 Example Approaches to Visual Search and Analysis of Compound-Complex Data 99
4.4.3.1 Visual Search in Research Data-Visual Query De?nition and Visualization of Search Results 99
4.4.3.2 Visual Search and Analysis of Spatio-temporal Data-Identi?cation of Interesting Events 100
4.4.3.3 Visual Analytics for Security 101
4.5 Research Challenges 103
4.5.1 Infrastructures 104
4.5.2 New Data Types 104
4.5.3 Search Problem and Comparative Visualization 104
4.5.4 User Guidance in the Visual Analysis Process 105
4.5.5 Benchmarking 105
4.6 Conclusions 106
References 106
Chapter 5: Dynamic Visual Analytics-Facing the Real-Time Challenge 109
5.1 Introduction 109
5.2 Background 111
5.2.1 Visual Analytics 111
5.2.2 Data Streams: Management and Automated Analysis 111
5.2.3 Time Series Visualization 112
5.3 Dynamic Visual Analytics 113
5.3.1 Requirements for Dynamic Visual Analytics Methods 113
5.3.2 The Role of the User in Dynamic Visual Analytics 115
5.4 Server Log Monitoring Application Example 116
Processing: 118
Update Models & Visualizations:
Display & Highlight:
Interact & Explore:
Notify & Adapt:
Feedback Loop: 119
5.5 Conclusions 119
References 119
Chapter 6: A Review of Uncertainty in Data Visualization 121
6.1 Introduction 121
6.2 Uncertainty Reference Model 123
6.3 Why Is Uncertainty so Hard? 124
6.4 Notation 128
6.5 Visualization of Uncertainty 128
6.5.1 Introduction 128
6.5.2 Point Data UP 130
6.5.3 Scalar Data US 130
6.5.3.1 Zero Dimensional Data US0 130
6.5.3.2 One Dimensional Data US1 131
6.5.3.3 Two Dimensional Data US2 131
6.5.3.4 Three Dimensional Data US3 135
6.5.4 Multi?eld Scalar Data kUS 137
6.5.4.1 Zero Dimensional Data UkS0 137
6.5.4.2 Higher Dimensional Data UkS> 0
6.5.5 Vector Data UV 137
6.5.5.1 Two Dimensional Data UV2 137
6.5.5.2 Three Dimensional Data UV3 139
6.6 Uncertainty of Visualization 139
6.6.1 Scalar Data ES 140
6.6.1.1 One Dimensional Data ES1 140
6.6.1.2 Two Dimensional Data ES2 140
6.6.1.3 Three Dimensional Data ES3 141
6.6.2 Multi?eld Scalar Data kES 143
6.6.3 Vector Data EV 143
6.7 Conclusions 144
References 145
Chapter 7: How to Draw a Graph, Revisited 150
7.1 Introduction 150
7.2 The Barycenter Algorithm 151
7.2.1 Tutte's General Approach 151
7.2.2 The Energy Model in Tutte's Algorithm 153
7.2.3 Tutte's Algorithm for Planar Graphs 153
7.2.4 Tutte's Algorithm as a Visualization Method 154
7.3 The Force Directed Approach 156
7.4 The Planarity Approach 157
7.4.1 Linear Time Algorithms for Planar Graphs 158
7.4.2 Planar Drawings with Good Vertex Resolution 159
7.4.3 Drawing Planar Graphs with Star-Shaped Faces 160
7.4.4 Drawing Nonplanar Graphs Using Planarity Based Methods 160
7.5 Remarks 162
References 163
Chapter 8: Using Extruded Volumes to Visualize Time-Series Datasets 166
8.1 Introduction 166
8.2 Project Description 167
8.2.1 Envision 167
8.2.2 Tools Used 168
8.2.3 Methodology 168
8.2.4 Data Extraction and Preparation 168
8.2.5 Rendering Techniques 169
8.2.6 Visualization User Interface 170
8.2.6.1 Slicing Tool 170
8.2.6.2 Alpha Control Tools 171
8.2.6.3 Highlighter Tool 172
8.2.6.4 Transitioning Tool 172
8.2.6.5 Orienteer Tool 173
8.3 Test Setup 174
8.4 Results and Discussion 174
8.4.1 Skagit Study Area: LULC_A 174
8.4.2 Apache-Sitgreaves National Forest Study Area: Vegetation Type 180
8.5 Future Work 184
8.6 Conclusion 185
References 187
Chapter 9: Event Structuring as a General Approach to Building Knowledge in Time-Based Collections 188
9.1 Introduction 188
9.2 De?ning Events, Creating Event Structures, Organizing the Time Dimension 189
9.3 Events in Space: 4D GIS 190
9.4 Events in a Narrative Structure 191
9.4.1 Human-Computer Generated Linear Narrative 192
9.5 Events in Non-geographic Information Spaces 193
9.6 Event Description Language for Linear Narrative 197
9.7 Towards a GTIS and TIS 198
References 200
Chapter 10: A Visual Analytics Approach for Protein Disorder Prediction 202
10.1 Introduction 203
10.2 Protein Disorder Prediction 204
10.3 Discriminant Analysis for Visualization 205
10.4 Visualization of Protein Disorder Data 207
10.4.1 Knowledge Discovery from Visualization 207
10.4.2 Visualizing the Discriminants 209
10.5 Classi?cation Evaluation and Discussion 209
10.6 Conclusion 212
References 212
Chapter 11: Visual Storytelling in Education Applied to Spatial-Temporal Multivariate Statistics Data 214
11.1 Introduction 215
11.2 Related Work 217
11.3 System Implementation 219
11.3.1 GAV Flash Framework 219
11.3.2 Integrated Snapshot Mechanism 222
11.4 Storytelling 223
11.4.1 Publisher and Vislets 224
11.5 Interactive Documents 226
11.6 Visual Storytelling in Education 229
11.7 Conclusions and Future Development 230
References 231
Part III: Interaction and User Interfaces 233
Chapter 12: Top Ten Interaction Challenges in Extreme-Scale Visual Analytics 234
12.1 Introduction 234
12.2 Related Work 235
12.2.1 Some Well-Known Extreme-Scale Data Problems Today 236
12.2.2 Extreme-Scale Data Visualization and Management 236
12.2.3 Top-Ten Visualization and Visual Interface Challenges in Literature 236
12.3 Three Fundamental Elements of Extreme-Scale Visual Analytics 237
12.4 Imminent Challenges of Interface and Interaction Design 237
12.4.1 In Situ Interactive Analysis 237
12.4.2 User-Driven Data Reduction 238
12.4.3 Scalability and Multi-level Hierarchy 238
12.4.4 Representation of Evidence and Uncertainty 239
12.4.5 Heterogeneous Data Fusion 239
12.4.6 Data Summarization and Triage for Interactive Query 240
12.4.7 Analytics of Temporally Evolving Features 240
12.4.8 The Human Bottleneck 241
12.4.9 Design and Engineering Development 241
12.4.10 The Renaissance of Conventional Wisdom 242
12.5 Evaluation and Likelihood of Success 242
12.6 Conclusions 243
References 243
Chapter 13: GUI 4D-The Role and the Impact of Visual, Multimedia and Multilingual User Interfaces in ICT Applications and Services for Users Coming from the Bottom of the Pyramid-First Concepts, Prototypes and Experiences 245
13.1 Introduction 246
13.2 Scope, De?nitions and Classi?cation 246
13.3 Design and Implementation 250
13.4 Requirements and Constraints-Implementation Framework 252
13.5 The SAP Strategy and Vision on GUI 4D's 255
13.5.1 Target Groups 255
13.5.2 Motivation and Mission of SAP Research Internet Applications and Services Africa (Pretoria, South Africa) 257
13.5.3 Examples and Case Studies from SAP Research Internet Applications and Services Africa (Pretoria) 258
13.5.3.1 Rustica 259
13.5.3.2 Smart Energy 259
13.5.3.3 Siyakhula Living Lab 260
13.6 Ongoing Projects and R& D Activities in GUI 4D's in Africa
13.6.1 Case Study-The African Cashew Initiative 260
13.6.1.1 Objectives 260
13.6.1.2 Use Cases 261
13.6.1.3 Piloting-Real Life Usage 262
13.6.1.4 Results 263
13.6.2 Other Interesting GUI 4D Research and Development Activities in Africa 265
13.6.3 Conclusions for GUI 4D 265
13.7 Target Applications and Markets 266
13.7.1 The Informal Sector in the "Bottom of the Pyramid" 266
13.7.1.1 Dependencies and Needs Between the Established Economy and Informal Economy 266
13.7.2 Global Agricultural Supply Chains-The Cashew Market as an Example 268
13.7.3 Market Potential 269
13.8 Future Research and Work to Be Done 269
13.9 Conclusions and Summary 270
References 271
Chapter 14: Emotion in Human-Computer Interaction 274
14.1 Introduction 274
14.2 Emotion Recognition 276
14.2.1 Physiological Background 276
14.2.2 Measuring Emotional Signs 278
14.2.2.1 Challenges 280
14.2.2.2 Requirements 280
14.2.3 The Emotion Recognition Pipeline 281
14.2.3.1 Data Pre-processing 282
14.2.3.2 Feature Extraction 282
14.2.3.3 Classi?cation 283
14.3 The EREC Emotion Recognition System 283
14.3.1 The EREC Sensor System 283
14.3.2 Data Interpretation 287
14.3.2.1 Data Pre-processing 287
14.3.2.2 Feature Extraction and Classi?cation 287
14.4 Applications 287
14.4.1 Affective Usability Evaluation Tool 288
14.4.1.1 The RealEYES Framework 288
14.4.1.2 Affective Extension to the RealEYES Framework 290
14.4.1.3 Visualizing Classi?cation Results 291
14.4.2 Affective E-Learning Environment 292
14.5 Conclusion and Further Prospects 294
References 294
Chapter 15: Applying Artistic Color Theories to Visualization 298
15.1 Introduction 298
15.2 Some Background on Color Theory 299
15.3 The Color Wheel for the RYB Painterly Set of Primary Colors 301
15.4 The Color Wheel for the RGB Model 303
15.5 Hue, Saturation and Brightness (HSL) & Hue, Saturation and Value (HSV) Models
15.6 Color Schemes 306
15.7 Color Wheel and Color Scheme Software Tools 308
15.8 Analyzing Digital Images with the Color Wheel and Color Schemes 309
15.9 Applying Color Scheme Concepts to Creating Visualizations 311
15.10 Conclusion 316
References 316
Chapter 16: e-Culture and m-Culture: The Way that Electronic, Computing and Mobile Devices are Changing the Nature of Art, Design and Culture 319
16.1 The Development of Esteem for Cultural Product Creators 320
16.2 Evolving Culture, with a Capital `C' 321
16.3 Technological In?uences 322
16.4 Connecting with the User-The CU in Culture 324
16.5 Mobile Paradigms Transforming Journalism 325
16.6 Narrative 327
16.7 Growing Pains in Mobile Technology 328
16.8 Technology, Communities and `Culture' 329
16.9 Where Next? 330
16.10 Wearable Computing and Communications 331
16.11 Some Conclusions 334
References 335
Part IV: Modeling and Geometry 337
Chapter 17: Shape Identi?cation in Temporal Data Sets 338
17.1 What Are Shapes? 339
17.2 Background 340
17.2.1 Shape De?nition 340
17.2.2 Shape Evaluation 341
17.3 Shape De?nitions 342
17.3.1 Line Shapes 343
17.3.2 Spike and Sink Shapes 344
17.3.3 Rise and Drop Shapes 345
17.3.4 Plateaus, Valleys and Gaps 346
17.4 TimeSearcher: Shape Search Edition 347
17.4.1 Interface 348
17.4.2 Spike and Sink Shape Identi?cation 349
17.4.3 Line Shape Identi?cation 351
17.4.4 Rise and Drop Shape Identi?cation 351
17.5 Conclusion 353
References 353
Chapter 18: SSD-C: Smooth Signed Distance Colored Surface Reconstruction 355
18.1 Introduction 355
18.2 Continuous Formulation 357
18.2.1 Surface Reconstruction 357
18.2.2 Color Map Reconstruction 359
18.3 Linearly Parameterized Families 360
18.4 Discretization with Discontinuous Function 361
18.5 Octree-Based Implementation 362
18.6 Evaluation of Surface Reconstruction Methods 363
18.7 Results 365
18.8 Conclusion 369
References 369
Chapter 19: Geometric Issues of Object Manipulation in Task Animation and Virtual Reality 371
19.1 Introduction 371
19.2 The Smart Object Approach 372
19.3 The Grasping Problem 374
19.3.1 Introduction 374
19.3.2 Heuristic Approach for Grasping 374
19.3.3 Large Objects and Multiple Agents 375
19.3.4 The Tubular Approach 378
19.3.5 Combining Smart Objects and the Tubular Grasp 380
19.3.6 Collision Detection 381
19.4 The Reaching Problem 383
19.5 Grasping in VR 385
19.5.1 Introduction 385
19.5.2 Haptic Feedback 386
19.5.2.1 Direct Mapping 386
19.5.2.2 Proxy Approach 388
19.5.3 Creating Geometric and Dynamic Environments 389
19.6 Conclusion 391
References 392
Chapter 20: An Analytical Approach to Dynamic Skin Deformation of Character Animation 395
20.1 Introduction 395
20.2 Mathematical Model and Analytical Solution 397
20.3 Relationships Between Curves and Skin Surfaces 402
20.3.1 Curve-Based Representation of Skin Surfaces 402
20.3.2 Curve-Based Deformation Control of Skin Surfaces 402
20.4 Skin Deformation Examples 404
20.5 Conclusions 405
References 405
Part V: Architecture and Displays 407
Chapter 21: The New Visualization Engine- The Heterogeneous Processor Unit 408
21.1 Introduction 408
21.2 Historical Overview 409
21.3 Moore's Law and Transistor Feature Size 412
21.4 Evolution of GPU Development 413
21.5 PC-Based GPUs 413
21.6 Mobile Devices GPUs 414
21.7 Introduction of the HPU 414
21.8 Evolution of Operating System Development 415
21.9 HPUs in Various Platforms 416
21.10 PCs 417
21.11 Game Consoles 417
21.12 Mobile Devices 419
21.13 Power Consumption 420
21.14 Evolution of GPU-Compute Development Environments 421
21.15 Examples of Multicore Processors 421
21.16 Programming GPU-SIMDs Represents a Challenge 422
21.17 HPU Programming Environments 422
21.18 The Programming Environment 424
21.19 When Is Parallel Processing Useful? 424
21.20 Visualization Systems and HPUs 425
21.21 Summary 426
References 426
Chapter 22: Smart Cloud Computing 427
22.1 Introduction 427
22.2 Cyberworlds 428
22.2.1 Set Theoretical Design 428
22.2.2 Topological Design 428
22.2.3 Functions 429
22.2.4 Equivalence Relations 429
22.2.5 A Quotient Space (an Identi?cation Space) 430
22.2.6 An Attaching Space (an Adjunction Space, or an Adjoining Space) 431
22.2.7 Restriction and Inclusion 431
22.2.8 Extensions and Retractions of Continuous Maps 431
22.2.9 Homotopy 432
22.2.10 Cellular Structured Spaces (Cellular Spaces) 433
22.2.11 An Incrementally Modular Abstraction Hierarchy 435
22.2.12 Fiber Bundles, Homotopy Lifting Property, and Homotopy Extension Property 436
22.3 Modeling of E-Business and E-Manufacturing 439
22.3.1 The Adjunction Space Level 440
22.3.1.1 A Case of Online Book Shopping in E-Commerce 440
22.3.1.2 A Case of Assembling for E-Manufacturing 442
22.3.2 Cellular Space Level 442
22.3.3 Seat Assembling 444
22.4 Conclusions 444
References 444
Chapter 23: Visualization Surfaces 446
23.1 The Value of Scale and Detail 446
23.2 Large Display Mechanisms: Projection 448
23.3 Large Display Mechanisms: Modular Flat Panels 450
23.4 Display System Architecture 451
23.5 Interaction 453
23.6 Future 454
References 455
Part VI: Virtual Reality and Augmented Reality 457
Chapter 24: The Development of Mobile Augmented Reality 458
24.1 Introduction 458
24.2 Program Development 460
24.2.1 Research Issues 460
24.2.2 Information Management 461
24.2.3 Development Iterations 463
24.3 Program Expansion 465
24.3.1 Further Research Issues 465
24.3.2 ONR Program Expansion 465
24.3.3 The "X-Ray Vision" Problem and the Perception of Depth 468
24.3.4 Integration of a Component-Based System 468
24.4 Ongoing Research 469
24.5 Predictions for the Future 470
24.5.1 Consumer Use 470
24.5.2 Tracking 471
24.5.3 Form Factor 472
24.6 Summary 473
References 473
Chapter 25: Multimodal Interfaces for Augmented Reality 476
25.1 Introduction 476
25.2 Related Work 477
25.3 Speech and Paddle Gesture 479
25.3.1 Multimodal System 479
25.3.2 Evaluation 481
25.4 Speech and Free-Hand Input 483
25.4.1 Evaluation 486
25.5 Lessons Learned 489
25.6 Conclusions and Future Work 490
References 491
Part VII: Technology Transfer 493
Chapter 26: Knowledge Exchange, Technology Transfer and the Academy 494
26.1 Introduction 494
26.2 The Bayh-Dole Act 495
26.3 Technology Transfer Systems in the USA 495
26.4 Technology Transfer in Germany-The Fraunhofer Model 496
26.5 Lambert Review 497
26.6 Case Studies 498
26.6.1 MIT, Cambridge and Tokyo 498
26.6.2 Johns Hopkins University 498
26.6.3 University of Utah 499
26.6.4 National Visualization and Analytics Centers 499
26.7 Challenges, Cultural and Social Issues 500
26.7.1 Time scale 500
26.7.2 Reward Models 500
26.7.3 Value of Applied Research 500
26.7.4 Technology Transfer Culture 501
26.7.5 Communication and Values 501
26.7.6 Differences Across Discipline Areas 501
26.7.7 Performance Metrics 502
26.7.8 Diversi?cation of Academic Mission 503
26.8 Conclusions 503
References 504
Chapter 27: Discovering and Transitioning Technology 505
27.1 Introduction 505
27.2 Projects 506
27.2.1 General Motors 506
27.2.2 The Boeing Company 506
27.2.3 Computer Graphics 507
27.2.3.1 Evolution 508
27.2.4 Human Model 508
27.2.4.1 Evolution 508
27.2.5 B-Spline Surface Rendering 509
27.2.5.1 Evolution 510
27.2.6 Solid Modeling 510
27.2.7 Fractals 510
27.2.7.1 Evolution 510
27.2.8 User Interface Management Systems 511
27.2.8.1 Evolution 512
27.2.9 Augmented Reality 512
27.2.9.1 Evolution 512
27.2.10 FlyThru/IVT 513
27.2.10.1 Evolution 514
27.2.11 Voxmap PointShell 514
27.2.11.1 Evolution 514
27.2.12 Massive Model Visualization 515
27.2.12.1 Evolution 515
27.2.13 Visual Analytics 516
27.2.13.1 Evolution 516
27.3 Observations 516
27.4 Implications 518
27.4.1 Sources of New Technology 519
27.4.2 Fragmented Technical Community 519
27.4.3 Business Climate 520
27.4.4 Immediate Return on Investment 520
27.5 One Successful Approach 521
27.6 Conclusion 521
References 522
Chapter 28: Technology Transfer at IBBT-EDM: Case Study in the Computer Graphics Domain 523
28.1 Interdisciplinary Institute for BroadBand Technology (IBBT) 524
28.1.1 Strategic Research 524
28.1.2 Cooperative Research 525
28.1.3 Living Labs 526
28.1.4 Venture 526
28.2 Expertise Centre for Digital Media (EDM) 527
28.3 ANDROME 528
28.4 Case Study: Ultra Pictura 528
28.4.1 Company Summary 528
28.4.2 From Idea to Business 529
28.4.3 Company Management 531
28.5 Conclusions 532
References 532
Chapter 29: Building Adoption of Visual Analytics Software 533
29.1 Introduction 533
29.2 The Technology Adoption Life Cycle 535
29.3 Adoption Challenges for Visual Analytics 537
29.4 Case Study: Moore's Life Cycle Applied to an Organization 541
29.5 Cultural Implications of Adoption 543
29.6 Recommendations for Building Visual Analytics Technology Adoption 544
29.6.1 Initiating the Adoption Process 545
29.6.2 Building Interest Among Innovators 547
29.6.3 Technology Adoption by Early Adopters 548
29.6.4 Adoption by the Early Majority 550
29.6.5 Adoption by the Late Majority and Laggards 551
29.6.6 Adaptive Approaches for Technology Adoption 552
29.7 Conclusion 552
References 553
Author Index 555