Ebook: Data Engineering: Mining, Information and Intelligence
Author: Terry M. Talley John R. Talburt Yupo Chan (auth.) Yupo Chan John Talburt Terry M. Talley (eds.)
- Tags: Database Management, Information Storage and Retrieval, Business Information Systems, Information Systems and Communication Service, Operation Research/Decision Theory, e-Commerce/e-business
- Series: International Series in Operations Research & Management Science 132
- Year: 2010
- Publisher: Springer US
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages i-xvi
Introduction....Pages 1-16
A Declarative Approach to Entity Resolution....Pages 17-38
Transitive Closure of Data Records: Application and Computation....Pages 39-75
Semantic Data Matching: Principles and Performance....Pages 77-90
Application of the Near Miss Strategy and Edit Distance to Handle Dirty Data....Pages 91-101
A Parallel General-Purpose Synthetic Data Generator1 ....Pages 103-117
A Grid Operating Environment for CDI....Pages 119-142
Parallel File Systems....Pages 143-168
Performance Modeling of Enterprise Grids....Pages 169-201
Delay Characteristics of Packet Switched Networks....Pages 203-223
Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach....Pages 225-243
Mining E-Documents to Uncover Structures....Pages 245-278
Designing a Flexible Framework for a Table Abstraction....Pages 279-314
Information Quality Framework for Verifiable Intelligence Products....Pages 315-333
Interactive Visualization of Large High-Dimensional Datasets....Pages 335-351
Image Watermarking Based on Pyramid Decomposition with CH Transform....Pages 353-387
Immersive Visualization of Cellular Structures....Pages 389-402
Visualization and Ontology of Geospatial Intelligence....Pages 403-429
Looking Ahead....Pages 431-439
Back Matter....Pages 441-447
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.
Content:
Front Matter....Pages i-xvi
Introduction....Pages 1-16
A Declarative Approach to Entity Resolution....Pages 17-38
Transitive Closure of Data Records: Application and Computation....Pages 39-75
Semantic Data Matching: Principles and Performance....Pages 77-90
Application of the Near Miss Strategy and Edit Distance to Handle Dirty Data....Pages 91-101
A Parallel General-Purpose Synthetic Data Generator1 ....Pages 103-117
A Grid Operating Environment for CDI....Pages 119-142
Parallel File Systems....Pages 143-168
Performance Modeling of Enterprise Grids....Pages 169-201
Delay Characteristics of Packet Switched Networks....Pages 203-223
Knowledge Discovery in Textual Databases: A Concept-Association Mining Approach....Pages 225-243
Mining E-Documents to Uncover Structures....Pages 245-278
Designing a Flexible Framework for a Table Abstraction....Pages 279-314
Information Quality Framework for Verifiable Intelligence Products....Pages 315-333
Interactive Visualization of Large High-Dimensional Datasets....Pages 335-351
Image Watermarking Based on Pyramid Decomposition with CH Transform....Pages 353-387
Immersive Visualization of Cellular Structures....Pages 389-402
Visualization and Ontology of Geospatial Intelligence....Pages 403-429
Looking Ahead....Pages 431-439
Back Matter....Pages 441-447
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