TY  - CHAP
T1  - Data-Intensive Analysis
T2  - THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business
Y1  - 2013
A1  - Oscar Corcho
A1  - van Hemert, Jano
ED  - Malcolm Atkinson
ED  - Rob Baxter
ED  - Peter Brezany
ED  - Oscar Corcho
ED  - Michelle Galea
ED  - Parsons, Mark
ED  - Snelling, David
ED  - van Hemert, Jano
KW  - data mining
KW  - Data-Analysis Experts
KW  - Data-Intensive Analysis
KW  - Knowledge Discovery
AB  - Part II: "Data-intensive Knowledge Discovery", focuses on the needs of data-analysis experts. It illustrates the problem-solving strategies appropriate for a data-rich world, without delving into the details of underlying technologies. It should engage and inform data-analysis specialists, such as statisticians, data miners, image analysts, bio-informaticians or chemo-informaticians, and generate  ideas pertinent to their application areas.    Chapter 5: "Data-intensive Analysis", introduces a set of common problems that data-analysis experts often encounter, by means of a set of scenarios of increasing levels of complexity. The scenarios typify knowledge discovery challenges and the presented solutions provide practical methods; a starting point for readers addressing their own data challenges.
JF  - THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business
PB  - John Wiley & Sons Ltd.
ER  - 

TY  - CHAP
T1  - The Data-Intensive Survival Guide
T2  - THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business
Y1  - 2013
A1  - Malcolm Atkinson
ED  - Malcolm Atkinson
ED  - Rob Baxter
ED  - Peter Brezany
ED  - Oscar Corcho
ED  - Michelle Galea
ED  - Parsons, Mark
ED  - Snelling, David
ED  - van Hemert, Jano
KW  - Data-Analysis Experts
KW  - Data-Intensive Architecture
KW  - Data-intensive Computing
KW  - Data-Intensive Engineers
KW  - Datascopes
KW  - Dispel
KW  - Domain Experts
KW  - Intellectual Ramps
KW  - Knowledge Discovery
KW  - Workflows
AB  - Chapter 3: "The data-intensive survival guide", presents an overview of all of the elements of the proposed data-intensive strategy. Sufficient detail is presented for readers to understand the principles and practice that we recommend. It should also provide a good preparation for readers who choose to sample later chapters. It introduces three professional viewpoints: domain experts, data-analysis experts, and data-intensive engineers. Success depends on a balanced approach that develops the capacity of all three groups. A data-intensive architecture provides a flexible framework for that balanced approach. This enables the three groups to build and exploit data-intensive processes that incrementally step from data to results. A language is introduced to describe these incremental data processes from all three points of view. The chapter introduces ‘datascopes’ as the productized data-handling environments and ‘intellectual ramps’ as the ‘on ramps’  for the highways from data to knowledge.
JF  - THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business
PB  - John Wiley & Sons Ltd.
ER  - 

TY  - CHAP
T1  - Problem Solving in Data-Intensive Knowledge Discovery
T2  - THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business
Y1  - 2013
A1  - Oscar Corcho
A1  - van Hemert, Jano
ED  - Malcolm Atkinson
ED  - Rob Baxter
ED  - Peter Brezany
ED  - Oscar Corcho
ED  - Michelle Galea
ED  - Parsons, Mark
ED  - Snelling, David
ED  - van Hemert, Jano
KW  - Data-Analysis Experts
KW  - Data-Intensive Analysis
KW  - Design Patterns for Knowledge Discovery
KW  - Knowledge Discovery
AB  - Chapter 6: "Problem solving in data-intensive knowledge discovery", on the basis of the previous scenarios, this chapter provides an overview of effective strategies in knowledge discovery, highlighting common problem-solving methods that apply in conventional contexts, and focusing on the similarities and differences of these methods.
JF  - THE DATA BONANZA: Improving Knowledge Discovery for Science, Engineering and Business
PB  - John Wiley & Sons Ltd.
ER  -