This book presents the most up-to-date opportunities and challenges emerging in knowledge discovery, helping readers develop the technical skills to design and develop data-intensive methods and processes. Offering an introduction to the current R and D efforts worldwide, the book includes examples and case studies with strategies for addressing a wide variety of data-intensive challenges. The book includes a discussion of the DISPEL language, its development, enactment, and applications as well as data-intensive beacons of success, focusing on methods in astronomy, interactive interpretation of environment data, and data-driven research in humanities. A must-have resource for researchers in industry, governmental organizations, and academia.

MALCOLM ATKINSON, PhD, is Professor of e-Science in the School of Informatics at the University of Edinburgh in Scotland. He is also Data-Intensive Research Group leader, Director of the e-Science Institute, IT architect for the ADMIRE and VERCE EU projects and UK e-Science Envoy. Professor Atkinson has been leading research projects for several decades and served on many advisory bodies.

CONTRIBUTORS xv

FOREWORD xvii

PREFACE xix

THE EDITORS xxix

PART I STRATEGIES FOR SUCCESS IN THE DIGITAL-DATA REVOLUTION 1

1. The Digital-Data Challenge 5

Malcolm Atkinson and Mark Parsons

1.1 The Digital Revolution / 5

1.2 Changing How We Think and Behave / 6

1.3 Moving Adroitly in this Fast-Changing Field / 8

1.4 Digital-Data Challenges Exist Everywhere / 8

1.5 Changing How We Work / 9

1.6 Divide and Conquer Offers the Solution / 10

1.7 Engineering Data-to-Knowledge Highways / 12

References / 13

2. The Digital-Data Revolution 15

Malcolm Atkinson

2.1 Data, Information, and Knowledge / 16

2.2 Increasing Volumes and Diversity of Data / 18

2.3 Changing the Ways We Work with Data / 28

References / 33

3. The Data-Intensive Survival Guide 37

Malcolm Atkinson

3.1 Introduction: Challenges and Strategy / 38

3.2 Three Categories of Expert / 39

3.3 The Data-Intensive Architecture / 41

3.4 An Operational Data-Intensive System / 42

3.5 Introducing DISPEL / 44

3.6 A Simple DISPEL Example / 45

3.7 Supporting Data-Intensive Experts / 47

3.8 DISPEL in the Context of Contemporary Systems / 48

3.9 Datascopes / 51

3.10 Ramps for Incremental Engagement / 54

3.11 Readers’ Guide to the Rest of This Book / 56

References / 58

4. Data-Intensive Thinking with DISPEL 61

Malcolm Atkinson

4.1 Processing Elements / 62

4.2 Connections / 64

4.3 Data Streams and Structure / 65

4.4 Functions / 66

4.5 The Three-Level Type System / 72

4.6 Registry, Libraries, and Descriptions / 81

4.7 Achieving Data-Intensive Performance / 86

4.8 Reliability and Control / 108

4.9 The Data-to-Knowledge Highway / 116

References / 121

PART II DATA-INTENSIVE KNOWLEDGE DISCOVERY 123

5. Data-Intensive Analysis 127

Oscar Corcho and Jano van Hemert

5.1 Knowledge Discovery in Telco Inc. / 128

5.2 Understanding Customers to Prevent Churn / 130

5.3 Preventing Churn Across Multiple Companies / 134

5.4 Understanding Customers by Combining Heterogeneous Public

and Private Data / 137

5.5 Conclusions / 144

References / 145

6. Problem Solving in Data-Intensive Knowledge Discovery 147

Oscar Corcho and Jano van Hemert

6.1 The Conventional Life Cycle of Knowledge Discovery / 148

6.2 Knowledge Discovery Over Heterogeneous Data Sources / 155

6.3 Knowledge Discovery from Private and Public, Structured and Nonstructured Data / 158

6.4 Conclusions / 162

References / 162

7. Data-Intensive Components and Usage Patterns 165

Oscar Corcho

7.1 Data Source Access and Transformation Components / 166

7.2 Data Integration Components / 172

7.3 Data Preparation and Processing Components / 173

7.4 Data-Mining Components / 174

7.5 Visualization and Knowledge Delivery Components / 176

References / 178

8. Sharing and Reuse in Knowledge Discovery 181

Oscar Corcho

8.1 Strategies for Sharing and Reuse / 182

8.2 Data Analysis Ontologies for Data Analysis Experts / 185

8.3 Generic Ontologies for Metadata Generation / 188

8.4 Domain Ontologies for Domain Experts / 189

8.5 Conclusions / 190

References / 191

PART III DATA-INTENSIVE ENGINEERING 193

9. Platforms for Data-Intensive Analysis 197

David Snelling

9.1 The Hourglass Reprise / 198

9.2 The Motivation for a Platform / 200

9.3 Realization / 201

References / 201

10. Definition of the DISPEL Language 203

Paul Martin and Gagarine Yaikhom

10.1 A Simple Example / 204

10.2 Processing Elements / 205

10.3 Data Streams / 213

10.4 Type System / 217

10.5 Registration / 222

10.6 Packaging / 224

10.7 Workflow Submission / 225

10.8 Examples of DISPEL / 227

10.9 Summary / 235

References / 236

11. DISPEL Development 237

Adrian Mouat and David Snelling

11.1 The Development Landscape / 237

11.2 Data-Intensive Workbenches / 239

11.3 Data-Intensive Component Libraries / 247

11.4 Summary / 248

References / 248

12. DISPEL Enactment 251

Chee Sun Liew, Amrey Krause, and David Snelling

12.1 Overview of DISPEL Enactment / 251

12.2 DISPEL Language Processing / 253

12.3 DISPEL Optimization / 255

12.4 DISPEL Deployment / 266

12.5 DISPEL Execution and Control / 268

References / 273

PART IV DATA-INTENSIVE APPLICATION EXPERIENCE 275

13. The Application Foundations of DISPEL 277

Rob Baxter

13.1 Characteristics of Data-Intensive Applications / 277

13.2 Evaluating Application Performance / 280

13.3 Reviewing the Data-Intensive Strategy / 283

14. Analytical Platform for Customer Relationship Management 287

Maciej Jarka and Mark Parsons

14.1 Data Analysis in the Telecoms Business / 288

14.2 Analytical Customer Relationship Management / 289

14.3 Scenario 1: Churn Prediction / 291

14.4 Scenario 2: Cross Selling / 293

14.5 Exploiting the Models and Rules / 296

14.6 Summary: Lessons Learned / 299

References / 299

15. Environmental Risk Management 301

Ladislav Hluch´y, Ondrej Habala, Viet Tran, and Branislav ? Simo

15.1 Environmental Modeling / 302

15.2 Cascading Simulation Models / 303

15.3 Environmental Data Sources and Their Management / 305

15.4 Scenario 1: ORAVA / 309

15.5 Scenario 2: RADAR / 313

15.6 Scenario 3: SVP / 318

15.7 New Technologies for Environmental Data Mining / 321

15.8 Summary: Lessons Learned / 323

References / 325

16. Analyzing Gene Expression Imaging Data in Developmental

Biology 327

Liangxiu Han, Jano van Hemert, Ian Overton, Paolo Besana, and

Richard Baldock

16.1 Understanding Biological Function / 328

16.2 Gene Image Annotation / 330

16.3 Automated Annotation of Gene Expression Images / 331

16.4 Exploitation and Future Work / 341

16.5 Summary / 345

References / 346

17. Data-Intensive Seismology: Research Horizons 353

Michelle Galea, Andreas Rietbrock, Alessandro Spinuso, and Luca Trani

17.1 Introduction / 354

17.2 Seismic Ambient Noise Processing / 356

17.3 Solution Implementation / 358

17.4 Evaluation / 369

17.5 Further Work / 372

17.6 Conclusions / 373

References / 375

PART V DATA-INTENSIVE BEACONS OF SUCCESS 377

18. Data-Intensive Methods in Astronomy 381

Thomas D. Kitching, Robert G. Mann, Laura E. Valkonen, Mark S. Holliman,

Alastair Hume, and Keith T. Noddle

18.1 Introduction / 381

18.2 The Virtual Observatory / 382

18.3 Data-Intensive Photometric Classification of Quasars / 383

18.4 Probing the Dark Universe with Weak Gravitational Lensing / 387

18.5 Future Research Issues / 392

18.6 Conclusions / 392

References / 393

19. The World at One’s Fingertips: Interactive

Interpretation of Environmental Data 395

Jon Blower, Keith Haines, and Alastair Gemmell

19.1 Introduction / 395

19.2 The Current State of the Art / 397

19.3 The Technical Landscape / 401

19.4 Interactive Visualization / 403

19.5 From Visualization to Intercomparison / 406

19.6 Future Development: The Environmental Cloud / 409

19.7 Conclusions / 411

References / 412

20. Data-Driven Research in the Humanities—the DARIAH

Research Infrastructure 417

Andreas Aschenbrenner, Tobias Blanke, Christiane Fritze, andWolfgang Pempe

20.1 Introduction / 417

20.2 The Tradition of Digital Humanities / 420

20.3 Humanities Research Data / 422

20.4 Use Case / 426

20.5 Conclusion and Future Development / 429

References / 430

21. Analysis of Large and Complex Engineering and Transport Data 431

Jim Austin

21.1 Introduction / 431

21.2 Applications and Challenges / 432

21.3 The Methods Used / 434

21.4 Future Developments / 438

21.5 Conclusions / 439

References / 440

22. Estimating Species Distributions—Across Space,

Through Time, and with Features of the Environment 441

Steve Kelling, Daniel Fink, Wesley Hochachka, Ken Rosenberg,

Robert Cook, Theodoros Damoulas, Claudio Silva,

and William Michener

22.1 Introduction / 442

22.2 Data Discovery, Access, and Synthesis / 443

22.3 Model Development / 448

22.4 Managing Computational Requirements / 449

22.5 Exploring and Visualizing Model Results / 450

22.6 Analysis Results / 452

22.7 Conclusion / 454

References / 456

PART VI THE DATA-INTENSIVE FUTURE 459

23. Data-Intensive Trends 461

Malcolm Atkinson and Paolo Besana

23.1 Reprise / 461

23.2 Data-Intensive Applications / 469

References / 476

24. Data-Rich Futures 477

Malcolm Atkinson

24.1 Future Data Infrastructure / 478

24.2 Future Data Economy / 485

24.3 Future Data Society and Professionalism / 489

References / 494

Appendix A: Glossary 499

Michelle Galea and Malcolm Atkinson

Appendix B: DISPEL Reference Manual 507

Paul Martin

Appendix C: Component Definitions 531

Malcolm Atkinson and Chee Sun Liew

INDEX 537

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