Ebook: The DATA Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business

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.Content:
Chapter 1 The Digital?Data Challenge (pages 5–13): Malcolm Atkinson and Mark Parsons
Chapter 2 The Digital?Data Revolution (pages 15–36): Malcolm Atkinson
Chapter 3 The Data?Intensive Survival Guide (pages 37–60): Malcolm Atkinson
Chapter 4 Data?Intensive Thinking with DISPEL (pages 61–122): Malcolm Atkinson
Chapter 5 Data?Intensive Analysis (pages 127–145): Oscar Corcho and Jano van Hemert
Chapter 6 Problem Solving in Data?Intensive Knowledge Discovery (pages 147–163): Oscar Corcho and Jano van Hemert
Chapter 7 Data?Intensive Components and Usage Patterns (pages 165–179): Oscar Corcho
Chapter 8 Sharing and Reuse in Knowledge Discovery (pages 181–192): Oscar Corcho
Chapter 9 Platforms for Data?Intensive Analysis (pages 197–201): David Snelling
Chapter 10 Definition of the DISPEL Language (pages 203–236): Paul Martin and Gagarine Yaikhom
Chapter 11 DISPEL Development (pages 237–249): Adrian Mouat and David Snelling
Chapter 12 DISPEL Enactment (pages 251–273): Chee Sun Liew, Amrey Krause and David Snelling
Chapter 13 The Application Foundations of DISPEL (pages 277–285): Rob Baxter
Chapter 14 Analytical Platform for Customer Relationship Management (pages 287–300): Maciej Jarka and Mark Parsons
Chapter 15 Environmental Risk Management (pages 301–326): Ladislav Hluchy, Ondrej Habala, Viet Tran and Branislav Simo
Chapter 16 Analyzing Gene Expression Imaging Data in Developmental Biology (pages 327–351): Liangxiu Han, Jano van Hemert, Ian Overton, Paolo Besana and Richard Baldock
Chapter 17 Data?Intensive Seismology: Research Horizons (pages 353–376): Michelle Galea, Andreas Rietbrock, Alessandro Spinuso and Luca Trani
Chapter 18 Data?Intensive Methods in Astronomy (pages 381–394): Thomas D. Kitching, Robert G. Mann, Laura E. Valkonen, Mark S. Holliman, Alastair Hume and Keith T. Noddle
Chapter 19 The World at One's Fingertips: Interactive Interpretation of Environmental Data (pages 395–416): Jon Blower, Keith Haines and Alastair Gemmell
Chapter 20 Data?Driven Research in the Humanities—the DARIAH Research Infrastructure (pages 417–430): Andreas Aschenbrenner, Tobias Blanke, Christiane Fritze and Wolfgang Pempe
Chapter 21 Analysis of Large and Complex Engineering and Transport Data (pages 431–440): Jim Austin
Chapter 22 Estimating Species Distributions—Across Space, Through Time, and with Features of the Environment (pages 441–458): Steve Kelling, Daniel Fink, Wesley Hochachka, Ken Rosenberg, Robert Cook, Theodoros Damoulas, Claudio Silva and William Michener
Chapter 23 Data?Intensive Trends (pages 459–476): Malcolm Atkinson and Paolo Besana
Chapter 24 Data?Rich Futures (pages 477–498): Malcolm Atkinson