{"product_id":"the-data-bonanza-9781118398647","title":"The Data Bonanza","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eComplete guidance for mastering the tools and techniques of the digital revolution\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections.\u003c\/p\u003e \u003cp\u003eEmphasizing data-intensive thinking and interdisciplinary collaboration, \u003ci\u003eThe Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business\u003c\/i\u003e examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-wor\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eCONTRIBUTORS xv\u003c\/p\u003e \u003cp\u003eFOREWORD xvii\u003c\/p\u003e \u003cp\u003ePREFACE xix\u003c\/p\u003e \u003cp\u003eTHE EDITORS xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I STRATEGIES FOR SUCCESS IN THE DIGITAL-DATA REVOLUTION 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. The Digital-Data Challenge 5\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMalcolm Atkinson and Mark Parsons\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Digital Revolution 5\u003c\/p\u003e \u003cp\u003e1.2 Changing How We Think and Behave 6\u003c\/p\u003e \u003cp\u003e1.3 Moving Adroitly in this Fast-Changing Field 8\u003c\/p\u003e \u003cp\u003e1.4 Digital-Data Challenges Exist Everywhere 8\u003c\/p\u003e \u003cp\u003e1.5 Changing How We Work 9\u003c\/p\u003e \u003cp\u003e1.6 Divide and Conquer Offers the Solution 10\u003c\/p\u003e \u003cp\u003e1.7 Engineering Data-to-Knowledge Highways 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. The Digital-Data Revolution 15\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMalcolm Atkinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Data, Information, and Knowledge 16\u003c\/p\u003e \u003cp\u003e2.2 Increasing Volumes and Diversity of Data 18\u003c\/p\u003e \u003cp\u003e2.3 Changing the Ways We Work with Data 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. The Data-Intensive Survival Guide 37\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMalcolm Atkinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction: Challenges and Strategy 38\u003c\/p\u003e \u003cp\u003e3.2 Three Categories of Expert 39\u003c\/p\u003e \u003cp\u003e3.3 The Data-Intensive Architecture 41\u003c\/p\u003e \u003cp\u003e3.4 An Operational Data-Intensive System 42\u003c\/p\u003e \u003cp\u003e3.5 Introducing DISPEL 44\u003c\/p\u003e \u003cp\u003e3.6 A Simple DISPEL Example 45\u003c\/p\u003e \u003cp\u003e3.7 Supporting Data-Intensive Experts 47\u003c\/p\u003e \u003cp\u003e3.8 DISPEL in the Context of Contemporary Systems 48\u003c\/p\u003e \u003cp\u003e3.9 Datascopes 51\u003c\/p\u003e \u003cp\u003e3.10 Ramps for Incremental Engagement 54\u003c\/p\u003e \u003cp\u003e3.11 Readers’ Guide to the Rest of This Book 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Data-Intensive Thinking with DISPEL 61\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMalcolm Atkinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Processing Elements 62\u003c\/p\u003e \u003cp\u003e4.2 Connections 64\u003c\/p\u003e \u003cp\u003e4.3 Data Streams and Structure 65\u003c\/p\u003e \u003cp\u003e4.4 Functions 66\u003c\/p\u003e \u003cp\u003e4.5 The Three-Level Type System 72\u003c\/p\u003e \u003cp\u003e4.6 Registry, Libraries, and Descriptions 81\u003c\/p\u003e \u003cp\u003e4.7 Achieving Data-Intensive Performance 86\u003c\/p\u003e \u003cp\u003e4.8 Reliability and Control 108\u003c\/p\u003e \u003cp\u003e4.9 The Data-to-Knowledge Highway 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II DATA-INTENSIVE KNOWLEDGE DISCOVERY 123\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Data-Intensive Analysis 127\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eOscar Corcho and Jano van Hemert\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Knowledge Discovery in Telco Inc. 128\u003c\/p\u003e \u003cp\u003e5.2 Understanding Customers to Prevent Churn 130\u003c\/p\u003e \u003cp\u003e5.3 Preventing Churn Across Multiple Companies 134\u003c\/p\u003e \u003cp\u003e5.4 Understanding Customers by Combining Heterogeneous Public and Private Data 137\u003c\/p\u003e \u003cp\u003e5.5 Conclusions 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Problem Solving in Data-Intensive Knowledge Discovery 147\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eOscar Corcho and Jano van Hemert\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 The Conventional Life Cycle of Knowledge Discovery 148\u003c\/p\u003e \u003cp\u003e6.2 Knowledge Discovery Over Heterogeneous Data Sources 155\u003c\/p\u003e \u003cp\u003e6.3 Knowledge Discovery from Private and Public, Structured and Nonstructured Data 158\u003c\/p\u003e \u003cp\u003e6.4 Conclusions 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Data-Intensive Components and Usage Patterns 165\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eOscar Corcho\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Data Source Access and Transformation Components 166\u003c\/p\u003e \u003cp\u003e7.2 Data Integration Components 172\u003c\/p\u003e \u003cp\u003e7.3 Data Preparation and Processing Components 173\u003c\/p\u003e \u003cp\u003e7.4 Data-Mining Components 174\u003c\/p\u003e \u003cp\u003e7.5 Visualization and Knowledge Delivery Components 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Sharing and Reuse in Knowledge Discovery 181\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eOscar Corcho\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Strategies for Sharing and Reuse 182\u003c\/p\u003e \u003cp\u003e8.2 Data Analysis Ontologies for Data Analysis Experts 185\u003c\/p\u003e \u003cp\u003e8.3 Generic Ontologies for Metadata Generation 188\u003c\/p\u003e \u003cp\u003e8.4 Domain Ontologies for Domain Experts 189\u003c\/p\u003e \u003cp\u003e8.5 Conclusions 190\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III DATA-INTENSIVE ENGINEERING 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Platforms for Data-Intensive Analysis 197\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDavid Snelling\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Hourglass Reprise 198\u003c\/p\u003e \u003cp\u003e9.2 The Motivation for a Platform 200\u003c\/p\u003e \u003cp\u003e9.3 Realization 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Definition of the DISPEL Language 203\u003c\/b\u003e\u003cbr\u003e \u003ci\u003ePaul Martin and Gagarine Yaikhom\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 A Simple Example 204\u003c\/p\u003e \u003cp\u003e10.2 Processing Elements 205\u003c\/p\u003e \u003cp\u003e10.3 Data Streams 213\u003c\/p\u003e \u003cp\u003e10.4 Type System 217\u003c\/p\u003e \u003cp\u003e10.5 Registration 222\u003c\/p\u003e \u003cp\u003e10.6 Packaging 224\u003c\/p\u003e \u003cp\u003e10.7 Workflow Submission 225\u003c\/p\u003e \u003cp\u003e10.8 Examples of DISPEL 227\u003c\/p\u003e \u003cp\u003e10.9 Summary 235\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. DISPEL Development 237\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAdrian Mouat and David Snelling\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 The Development Landscape 237\u003c\/p\u003e \u003cp\u003e11.2 Data-Intensive Workbenches 239\u003c\/p\u003e \u003cp\u003e11.3 Data-Intensive Component Libraries 247\u003c\/p\u003e \u003cp\u003e11.4 Summary 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. DISPEL Enactment 251\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eChee Sun Liew, Amrey Krause, and David Snelling\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Overview of DISPEL Enactment 251\u003c\/p\u003e \u003cp\u003e12.2 DISPEL Language Processing 253\u003c\/p\u003e \u003cp\u003e12.3 DISPEL Optimization 255\u003c\/p\u003e \u003cp\u003e12.4 DISPEL Deployment 266\u003c\/p\u003e \u003cp\u003e12.5 DISPEL Execution and Control 268\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV DATA-INTENSIVE APPLICATION EXPERIENCE 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. The Application Foundations of DISPEL 277\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eRob Baxter\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Characteristics of Data-Intensive Applications 277\u003c\/p\u003e \u003cp\u003e13.2 Evaluating Application Performance 280\u003c\/p\u003e \u003cp\u003e13.3 Reviewing the Data-Intensive Strategy 283\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Analytical Platform for Customer Relationship Management 287\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMaciej Jarka and Mark Parsons\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Data Analysis in the Telecoms Business 288\u003c\/p\u003e \u003cp\u003e14.2 Analytical Customer Relationship Management 289\u003c\/p\u003e \u003cp\u003e14.3 Scenario 1: Churn Prediction 291\u003c\/p\u003e \u003cp\u003e14.4 Scenario 2: Cross Selling 293\u003c\/p\u003e \u003cp\u003e14.5 Exploiting the Models and Rules 296\u003c\/p\u003e \u003cp\u003e14.6 Summary: Lessons Learned 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Environmental Risk Management 301\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eLadislav Hluchy, Ondrej Habala, Viet Tran, and Branislav Simo\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Environmental Modeling 302\u003c\/p\u003e \u003cp\u003e15.2 Cascading Simulation Models 303\u003c\/p\u003e \u003cp\u003e15.3 Environmental Data Sources and Their Management 305\u003c\/p\u003e \u003cp\u003e15.4 Scenario 1: ORAVA 309\u003c\/p\u003e \u003cp\u003e15.5 Scenario 2: RADAR 313\u003c\/p\u003e \u003cp\u003e15.6 Scenario 3: SVP 318\u003c\/p\u003e \u003cp\u003e15.7 New Technologies for Environmental Data Mining 321\u003c\/p\u003e \u003cp\u003e15.8 Summary: Lessons Learned 323\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Analyzing Gene Expression Imaging Data in Developmental Biology 327\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eLiangxiu Han, Jano van Hemert, Ian Overton, Paolo Besana, and Richard Baldock\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Understanding Biological Function 328\u003c\/p\u003e \u003cp\u003e16.2 Gene Image Annotation 330\u003c\/p\u003e \u003cp\u003e16.3 Automated Annotation of Gene Expression Images 331\u003c\/p\u003e \u003cp\u003e16.4 Exploitation and Future Work 341\u003c\/p\u003e \u003cp\u003e16.5 Summary 345\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17. Data-Intensive Seismology: Research Horizons 353\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMichelle Galea, Andreas Rietbrock, Alessandro Spinuso, and Luca Trani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 354\u003c\/p\u003e \u003cp\u003e17.2 Seismic Ambient Noise Processing 356\u003c\/p\u003e \u003cp\u003e17.3 Solution Implementation 358\u003c\/p\u003e \u003cp\u003e17.4 Evaluation 369\u003c\/p\u003e \u003cp\u003e17.5 Further Work 372\u003c\/p\u003e \u003cp\u003e17.6 Conclusions 373\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V DATA-INTENSIVE BEACONS OF SUCCESS 377\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18. Data-Intensive Methods in Astronomy 381\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eThomas D. Kitching, Robert G. Mann, Laura E. Valkonen, Mark S. Holliman, Alastair Hume, and Keith T. Noddle\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 381\u003c\/p\u003e \u003cp\u003e18.2 The Virtual Observatory 382\u003c\/p\u003e \u003cp\u003e18.3 Data-Intensive Photometric Classification of Quasars 383\u003c\/p\u003e \u003cp\u003e18.4 Probing the Dark Universe with Weak Gravitational Lensing 387\u003c\/p\u003e \u003cp\u003e18.5 Future Research Issues 392\u003c\/p\u003e \u003cp\u003e18.6 Conclusions 392\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19. The World at One's Fingertips: Interactive Interpretation of Environmental Data 395\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eJon Blower, Keith Haines, and Alastair Gemmell\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 395\u003c\/p\u003e \u003cp\u003e19.2 The Current State of the Art 397\u003c\/p\u003e \u003cp\u003e19.3 The Technical Landscape 401\u003c\/p\u003e \u003cp\u003e19.4 Interactive Visualization 403\u003c\/p\u003e \u003cp\u003e19.5 From Visualization to Intercomparison 406\u003c\/p\u003e \u003cp\u003e19.6 Future Development: The Environmental Cloud 409\u003c\/p\u003e \u003cp\u003e19.7 Conclusions 411\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20. Data-Driven Research in the Humanities—the DARIAH Research Infrastructure 417\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAndreas Aschenbrenner, Tobias Blanke, Christiane Fritze, andWolfgang Pempe\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 417\u003c\/p\u003e \u003cp\u003e20.2 The Tradition of Digital Humanities 420\u003c\/p\u003e \u003cp\u003e20.3 Humanities Research Data 422\u003c\/p\u003e \u003cp\u003e20.4 Use Case 426\u003c\/p\u003e \u003cp\u003e20.5 Conclusion and Future Development 429\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21. Analysis of Large and Complex Engineering and Transport Data 431\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eJim Austin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 431\u003c\/p\u003e \u003cp\u003e21.2 Applications and Challenges 432\u003c\/p\u003e \u003cp\u003e21.3 The Methods Used 434\u003c\/p\u003e \u003cp\u003e21.4 Future Developments 438\u003c\/p\u003e \u003cp\u003e21.5 Conclusions 439\u003c\/p\u003e \u003cp\u003eReferences 440\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22. Estimating Species Distributions—Across Space, Through Time, and with Features of the Environment 441\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eSteve Kelling, Daniel Fink, Wesley Hochachka, Ken Rosenberg, Robert Cook, Theodoros Damoulas, Claudio Silva, and William Michener\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 442\u003c\/p\u003e \u003cp\u003e22.2 Data Discovery, Access, and Synthesis 443\u003c\/p\u003e \u003cp\u003e22.3 Model Development 448\u003c\/p\u003e \u003cp\u003e22.4 Managing Computational Requirements 449\u003c\/p\u003e \u003cp\u003e22.5 Exploring and Visualizing Model Results 450\u003c\/p\u003e \u003cp\u003e22.6 Analysis Results 452\u003c\/p\u003e \u003cp\u003e22.7 Conclusion 454\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VI THE DATA-INTENSIVE FUTURE 459\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23. Data-Intensive Trends 461\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMalcolm Atkinson and Paolo Besana\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Reprise 461\u003c\/p\u003e \u003cp\u003e23.2 Data-Intensive Applications 469\u003c\/p\u003e \u003cp\u003e24. Data-Rich Futures 477\u003cbr\u003e Malcolm Atkinson\u003c\/p\u003e \u003cp\u003e24.1 Future Data Infrastructure 478\u003c\/p\u003e \u003cp\u003e24.2 Future Data Economy 485\u003c\/p\u003e \u003cp\u003e24.3 Future Data Society and Professionalism 489\u003c\/p\u003e \u003cp\u003eReferences 494\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Glossary 499\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMichelle Galea and Malcolm Atkinson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: DISPEL Reference Manual 507\u003c\/b\u003e\u003cbr\u003e \u003ci\u003ePaul Martin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C: Component Definitions 531\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMalcolm Atkinson and Chee Sun Liew\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eINDEX 537\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406864458071,"sku":"9781118398647","price":89.06,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118398647.jpg?v=1730497377","url":"https:\/\/bookcurl.com\/products\/the-data-bonanza-9781118398647","provider":"Book Curl","version":"1.0","type":"link"}