© 2010

Data Engineering

Mining, Information and Intelligence

  • Yupo Chan
  • John Talburt
  • Terry M. Talley

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 132)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Terry M. Talley, John R. Talburt, Yupo Chan
    Pages 1-16
  3. Tanton H. Gibbs
    Pages 17-38
  4. Wing Ning Li, Roopa Bheemavaram, Xiaojun Zhang
    Pages 39-75
  5. Russell Deaton, Thao Doan, Tom Schweiger
    Pages 77-90
  6. Cihan Varol, Coskun Bayrak, Rick Wagner, Dana Goff
    Pages 91-101
  7. Joseph E. Hoag, Craig W. Thompson
    Pages 103-117
  8. Terry M. Talley
    Pages 119-142
  9. Robert Ross, Philip Carns, David Metheny
    Pages 143-168
  10. Doug L. Hoffman, Amy Apon, Larry Dowdy, Baochuan Lu, Nathan Hamm, Linh Ngo et al.
    Pages 169-201
  11. Mutlu Mete, Nurcan Yuruk, Xiaowei Xu, Daniel Berleant
    Pages 225-243
  12. Azita Bahrami
    Pages 245-278
  13. H. Conrad Cunningham, Yi Liu, Jingyi Wang
    Pages 279-314
  14. Hongwei Zhu, Richard Y. Wang
    Pages 315-333
  15. R. Kountchev, M. Milanova, Vl. Todorov, R. Kountcheva
    Pages 353-387
  16. Sinan Kockara, Nawab Ali, Serhan Dagtas
    Pages 389-402
  17. Yupo Chan, John Talburt, Terry Talley
    Pages 431-439

About this book


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.


Analytics association classification data quality database entity resolution grid computing performance visualization

Editors and affiliations

  • Yupo Chan
    • 1
  • John Talburt
    • 2
  • Terry M. Talley
    • 3
  1. 1.Dept. Systems Engineering, Donaghey College of Info Sci.University of ArkansasLittle RockUSA
  2. 2.Dept. Information ScienceUniversity of Arkansas, Little RockLittle RockUSA
  3. 3.Acxiom CorporationConwayUSA

Bibliographic information