Atkinson M., Baxter R., Brezany P., Corcho O., Galea M., Parsons M., Snelling D., Hemmert J. (eds.) The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business

Wiley, 2013. — 576 p. — ISBN: 978-1-118-39864-7.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.Strategies for success in the digital-data revolution
The Digital-Data Challenge
The Digital-Data Revolution
The Data-Intensive Survival Guide
Data-Intensive Thinking with DISPEL
Data-intensive knowledge discovery
Data-Intensive Analysis
Problem Solving in Data-Intensive Knowledge Discovery
Data-Intensive Components and Usage Patterns
Sharing and Reuse in Knowledge Discovery
Data-intensive engineering
Platforms for Data-Intensive Analysis
Definition of the DISPEL language
DISPEL development
DISPEL enactment
Data-intensive application experience
The Application Foundations of DISPEL
Analytical Platform for Customer Relationship Management
Environmental Risk Management
Analyzing Gene Expression Imaging Data in Developmental Biology
Data-Intensive Seismology: Research Horizons
Data-intensive beacons of success
Data-Intensive Methods in Astronomy
The World at One's Fingertips: Interactive Interpretation of Environmental Data
Data-Driven Research in the Humanities — the Dariah research Infrastructure
Analysis of Large and Complex Engineering and Transport Data
Estimating Species Distributions — Across Space, Through Time, and with Features of the Environment
The data-intensive future
Data-Intensive Trends
Appendix A: glossary
Appendix B: dispel reference Manual
Appendix C: component Definitions