• Terry M. Talley
  • John R. Talburt
  • Yupo Chan
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 132)


Many companies and organizations face the common problem illustrated in Figure 1.1. The challenge is to take data from the real world and convert it into a model that can be used for decision making. For example, the model can be used as a tool to drive campaigns. The purpose of these campaigns is to affect the real world in a positive way from the perspective of the organization running the campaign. The general process is to collect data from a number of sources, then integrate that data into a consistent and logically related set of data. The integrated data is stored in a repository. This repository is often called a data warehouse and is often stored in a commercial relational database. Using the data, mathematical techniques, and algorithms, a model of the real world is constructed to support the decision making process. A variety of campaign management tools then use the model to drive campaigns executed in the real world.


Data Integration Incoming Data Global Ranking Entity Resolution Model Repository 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Bockman B, Wolf D (2002) “MPIGALib: A Library for Island Model Parallel Genetic Algorithms.” Working Paper, CSCE Department, Pacific Lutheran University, May 28.Google Scholar
  2. Bramel J, Simchi-Levi D (1997) The Logic of Logistics, Springer-Verlag, New York-Berlin.MATHCrossRefGoogle Scholar
  3. Conway T (2003) “Parallel Processing on the Cheap: Using Unix Pipes to Run SAS® Programs in Parallel,” Ted Conway Consulting, Inc., Chicago, IL, SUGI 28 Conference, SAS.Google Scholar
  4. Fisher CW, Kingma BR (2001) Criticality of data quality as exemplified in two disasters. Information and Management, 39(2001), 109-116.CrossRefGoogle Scholar
  5. Fourer R (2001) “Linear Programming Solver or Modeling: Popular OR tool can take different approaches to reach common goal.” OR/MS Today, August, pp. 58-68.Google Scholar
  6. Hoeffding W (1963) “Probability Inequalities for Sums of Bounded Random Variables.” Journal of the American Statistical Association, pp 13-30.Google Scholar
  7. Maxwell DT (2002). Decision Analysis: Aiding Insight VI. OR/MS Today June, 44-51.sGoogle Scholar
  8. Nievergelt J, Hinterberger, H; Sevcik, KC (1984). “The Grid file: An Adaptable, Symmetric Multikey File Structure.” ACM Transactions on Database Systems, Vol. 9, No. 1, pp. 38-71.CrossRefGoogle Scholar
  9. Redman TC (1998) The Impact of Poor Data Quality on the Typical Enterprise. Communications of the ACM, 41(2), 79-82.CrossRefGoogle Scholar
  10. Wang RY, Strong DM (1996) Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5-34.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Terry M. Talley
    • 1
  • John R. Talburt
    • 2
  • Yupo Chan
    • 3
  1. 1.Acxiom CorporationConwayUSA
  2. 2.Department of Information ScienceUniversity of Arkansas at Little RockLittle RockUSA
  3. 3.Department of Systems EngineeringUniversity of Arkansas at Little RockLittle RockUSA

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