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E-grāmata: Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business

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Complete guidance for mastering the tools and techniques of the digital revolution

With 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.

Emphasizing data-intensive thinking and interdisciplinary collaboration, The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business 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-world systems, the book:





Outlines the concepts and rationale for implementing data-intensive computing in organizations Covers from the ground up problem-solving strategies for data analysis in a data-rich world Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL Features in-depth case studies in customer relations, environmental hazards, seismology, and more Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering Includes sample program snippets throughout the text as well as additional materials on a companion website

The Data Bonanza is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.

Papildus informācija

Commended for PROSE (Engineering/Technology) 2013.
Contributors xv
Foreword xvii
Preface xix
The Editors xxix
PART I Strategies For Success In The Digital-Data Revolution
1(122)
1 The Digital-Data Challenge
5(10)
Malcolm Atkinson
Mark Parsons
1.1 The Digital Revolution
5(1)
1.2 Changing How We Think and Behave
6(2)
1.3 Moving Adroitly in this Fast-Changing Field
8(1)
1.4 Digital-Data Challenges Exist Everywhere
8(1)
1.5 Changing-, How We Work
9(1)
1.6 Divide and Conquer Offers the Solution
10(2)
1.7 Engineering Data-to-Knowledge Highways
12(3)
References
13(2)
2 The Digital-Data Revolution
15(22)
Malcolm Atkinson
2.1 Data, Information, and Knowledge
16(2)
2.2 Increasing Volumes and Diversity of Data
18(10)
2.3 Changing the Ways We Work with Data
28(9)
References
33(4)
3 The Data-Intensive Survival Guide
37(24)
Malcolm Atkinson
3.1 Introduction: Challenges and Strategy
38(1)
3.2 Three Categories of Expert
39(2)
3.3 The Data-Intensive Architecture
41(1)
3.4 An Operational Data-Intensive System
42(2)
3.5 Introducing DISPEL
44(1)
3.6 A Simple DISPEL Example
45(2)
3.7 Supporting Data-Intensive Experts
47(1)
3.8 DISPEL in the Context of Contemporary Systems
48(3)
3.9 Datascopes
51(3)
3.10 Ramps for Incremental Engagement
54(2)
3.11 Readers' Guide to the Rest of This Book
56(5)
References
58(3)
4 Data-Intensive Thinking with DISPEL
61(62)
Malcolm Atkinson
4.1 Processing Elements
62(2)
4.2 Connections
64(1)
4.3 Data Streams and Structure
65(1)
4.4 Functions
66(6)
4.5 The Three-Level Type System
72(9)
4.6 Registry, Libraries, and Descriptions
81(5)
4.7 Achieving Data-Intensive Performance
86(22)
4.8 Reliability and Control
108(8)
4.9 The Data-to-Knowledge Highway
116(7)
References
121(2)
PART II Data-Intensive Knowledge Discovery
123(70)
5 Data-Intensive Analysis
127(20)
Oscar Corcho
Jano van Hemert
5.1 Knowledge Discovery in Telco Inc.
128(2)
5.2 Understanding Customers to Prevent Churn
130(4)
5.3 Preventing Churn Across Multiple Companies
134(3)
5.4 Understanding Customers by Combining Heterogeneous Public and Private Data
137(7)
5.5 Conclusions
144(3)
References
145(2)
6 Problem Solving in Data-Intensive Knowledge Discovery
147(18)
Oscar Corcho
Jano van Hemert
6.1 The Conventional Life Cycle of Knowledge Discovery
148(7)
6.2 Knowledge Discovery Over Heterogeneous Data Sources
155(3)
6.3 Knowledge Discovery from Private and Public, Structured and Nonstructured Data
158(4)
6.4 Conclusions
162(3)
References
162(3)
7 Data-Intensive Components and Usage Patterns
165(16)
Oscar Corcho
7.1 Data Source Access and Transformation Components
166(6)
7.2 Data Integration Components
172(1)
7.3 Data Preparation and Processing Components
173(1)
7.4 Data-Mining Components
174(2)
7.5 Visualization and Knowledge Delivery Components
176(5)
References
178(3)
8 Sharing and Reuse in Knowledge Discovery
181(12)
Oscar Corcho
8.1 Strategies for Sharing and Reuse
182(3)
8.2 Data Analysis Ontologies for Data Analysis Experts
185(3)
8.3 Generic Ontologies for Metadata Generation
188(1)
8.4 Domain Ontologies for Domain Experts
189(1)
8.5 Conclusions
190(3)
References
191(2)
PART III Data-Intensive Engineering
193(82)
9 Platforms for Data-Intensive Analysis
197(6)
David Snelling
9.1 The Hourglass Reprise
198(2)
9.2 The Motivation for a Platform
200(1)
9.3 Realization
201(2)
References
201(2)
10 Definition of the DISPEL Language
203(34)
Paul Martin
Gagarine Yaikhom
10.1 A Simple Example
204(1)
10.2 Processing Elements
205(8)
10.3 Data Streams
213(4)
10.4 Type System
217(5)
10.5 Registration
222(2)
10.6 Packaging
224(1)
10.7 Workflow Submission
225(2)
10.8 Examples of DISPEL
227(8)
10.9 Summary
235(2)
References
236(1)
11 DISPEL Development
237(14)
Adrian Mouat
David Snelling
11.1 The Development Landscape
237(2)
11.2 Data-Intensive Workbenches
239(8)
11.3 Data-Intensive Component Libraries
247(1)
11.4 Summary
248(3)
References
248(3)
12 DISPEL Enactment
251(24)
Chee Sun Liew
Amrey Krause
David Snelling
12.1 Overview of DISPEL Enactment
251(2)
12.2 DISPEL Language Processing
253(2)
12.3 DISPEL Optimization
255(11)
12.4 DISPEL Deployment
266(2)
12.5 DISPEL Execution and Control
268(7)
References
273(2)
PART IV Data-Intensive Application Experience
275(102)
13 The Application Foundations of DISPEL
277(10)
Rob Baxter
13.1 Characteristics of Data-Intensive Applications
277(3)
13.2 Evaluating Application Performance
280(3)
13.3 Reviewing the Data-Intensive Strategy
283(4)
14 Analytical Platform for Customer Relationship Management
287(14)
Maciej Jarka
Mark Parsons
14.1 Data Analysis in the Telecoms Business
288(1)
14.2 Analytical Customer Relationship Management
289(2)
14.3 Scenario 1: Churn Prediction
291(2)
14.4 Scenario 2: Cross Selling
293(3)
14.5 Exploiting the Models and Rules
296(3)
14.6 Summary: Lessons Learned
299(2)
References
299(2)
15 Environmental Risk Management
301(26)
Ladislav Hluchy
Ondrej Habala
Viet Tran
Branislav Simo
15.1 Environmental Modeling
302(1)
15.2 Cascading Simulation Models
303(2)
15.3 Environmental Data Sources and Their Management
305(4)
15.4 Scenario 1: ORAVA
309(4)
15.5 Scenario 2: RADAR
313(5)
15.6 Scenario 3: SVP
318(3)
15.7 New Technologies for Environmental Data Mining
321(2)
15.8 Summary: Lessons Learned
323(4)
References
325(2)
16 Analyzing Gene Expression Imaging Data in Developmental Biology
327(26)
Liangxiu Han
Jano van Hemert
Ian Overton
Paolo Besana
Richard Baldock
16.1 Understanding Biological Function
328(2)
16.2 Gene Image Annotation
330(1)
16.3 Automated Annotation of Gene Expression Images
331(10)
16.4 Exploitation and Future Work
341(4)
16.5 Summary
345(8)
References
346(7)
17 Data-Intensive Seismology: Research Horizons
353(24)
Michelle Galea
Andreas Rietbrock
Alessandro Spinuso
Luca Trani
17.1 Introduction
354(2)
17.2 Seismic Ambient Noise Processing
356(2)
17.3 Solution Implementation
358(11)
17.4 Evaluation
369(3)
17.5 Further Work
372(1)
17.6 Conclusions
373(4)
References
375(2)
PART V Data-Intensive Beacons Of Success
377(82)
18 Data-Intensive Methods in Astronomy
381(14)
Thomas D. Kitching
Robert G. Mann
Laura E. Valkonen
Mark S. Holliman
Alastair Hume
Keith T. Noddle
18.1 Introduction
381(1)
18.2 The Virtual Observatory
382(1)
18.3 Data-Intensive Photometric Classification of Quasars
383(4)
18.4 Probing the Dark Universe with Weak Gravitational Lensing
387(5)
18.5 Future Research Issues
392(1)
18.6 Conclusions
392(3)
References
393(2)
19 The World at One's Fingertips: Interactive Interpretation of Environmental Data
395(22)
Jon Blower
Keith Haines
Alastair Gemmell
19.1 Introduction
395(2)
19.2 The Current State of the Art
397(4)
19.3 The Technical Landscape
401(2)
19.4 Interactive Visualization
403(3)
19.5 From Visualization to Intercomparison
406(3)
19.6 Future Development: The Environmental Cloud
409(2)
19.7 Conclusions
411(6)
References
412(5)
20 Data-Driven Research in the Humanities-the DARIAH Research Infrastructure
417(14)
Andreas Aschenbrenner
Tobias Blanke
Christiane Fritze
Wolfgang Pempe
20.1 Introduction
417(3)
20.2 The Tradition of Digital Humanities
420(2)
20.3 Humanities Research Data
422(4)
20.4 Use Case
426(3)
20.5 Conclusion and Future Development
429(2)
References
430(1)
21 Analysis of Large and Complex Engineering and Transport Data
431(10)
Jim Austin
21.1 Introduction
431(1)
21.2 Applications and Challenges
432(2)
21.3 The Methods Used
434(4)
21.4 Future Developments
438(1)
21.5 Conclusions
439(2)
References
440(1)
22 Estimating Species Distributions-Across Space, Through Time, and with Features of the Environment
441(18)
Steve Kelling
Daniel Fink
Wesley Hochachka
Ken Rosenberg
Robert Cook
Theodoros Damoulas
Claudio Silva
William Michener
22.1 Introduction
442(1)
22.2 Data Discovery, Access, and Synthesis
443(5)
22.3 Model Development
448(1)
22.4 Managing Computational Requirements
449(1)
22.5 Exploring and Visualizing Model Results
450(2)
22.6 Analysis Results
452(2)
22.7 Conclusion
454(5)
References
456(3)
PART VI The Data-Intensive Future
459(40)
23 Data-Intensive Trends
461(16)
Malcolm Atkinson
Paolo Besana
23.1 Reprise
461(8)
23.2 Data-Intensive Applications
469(8)
References
476(1)
24 Data-Rich Futures
477(22)
Malcolm Atkinson
24.1 Future Data Infrastructure
478(7)
24.2 Future Data Economy
485(4)
24.3 Future Data Society and Professionalism
489(10)
References
494(5)
Appendix A: Glossary 499(8)
Michelle Galea
Malcolm Atkinson
Appendix B: DISPEL Reference Manual 507(24)
Paul Martin
Appendix C: Component Definitions 531(6)
Malcolm Atkinson
Chee Sun Liew
Index 537
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.
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