Data Engineering: Mining, Information and Intelligence: 132 (International Series in Operations Research & Management Science, 132) - Image 1

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Springer Data Engineering: Mining, Information and Intelligence: 132 (International Series in Operations Research & Management Science, 132)

Name: Data Engineering: Mining, Information and Intelligence: 132 (International Series in Operations Research & Management Science, 132)
Brand: Springer
Price: 79.3 GBP
Availability: InStock

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Description

DATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter. The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 1461424763
Category: Books > Subjects > Business, Finance & Law > Management > Information Management
Domain: Amazon UK
Release Date: 25 February 2012
Listed Since: 16 December 2011

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Springer Data Engineering: Mining, Information and Intelligence: 132 (International Series in Operations Research & Management Science, 132)

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