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A Framework for Reconciling Attribute Values from Multiple Data Sources

Author

Listed:
  • Zhengrui Jiang

    (College of Business, University of North Alabama, Florence, Alabama 35632)

  • Sumit Sarkar

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083)

  • Prabuddha De

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47907)

  • Debabrata Dey

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

Abstract

Because of the heterogeneous nature of different data sources, data integration is often one of the most challenging tasks in managing modern information systems. While the existing literature has focused on problems such as schema integration and entity identification, it has largely overlooked a basic question: When an attribute value for a real-world entity is recorded differently in different databases, how should the "best" value be chosen from the set of possible values? This paper provides an answer to this question. We first show how a probability distribution over a set of possible values can be derived. We then demonstrate how these probabilities can be used to solve a given decision problem by minimizing the total cost of type I, type II, and misrepresentation errors. Finally, we propose a framework for integrating multiple data sources when a single "best" value has to be chosen and stored for every attribute of an entity.

Suggested Citation

  • Zhengrui Jiang & Sumit Sarkar & Prabuddha De & Debabrata Dey, 2007. "A Framework for Reconciling Attribute Values from Multiple Data Sources," Management Science, INFORMS, vol. 53(12), pages 1946-1963, December.
    • Handle: RePEc:inm:ormnsc:v:53:y:2007:i:12:p:1946-1963
    DOI: 10.1287/mnsc.1070.0745
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    References listed on IDEAS

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    1. Debabrata Dey & Sumit Sarkar & Prabuddha De, 1998. "A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases," Management Science, INFORMS, vol. 44(10), pages 1379-1395, October.
    2. Ramayya Krishnan & James Peters & Rema Padman & David Kaplan, 2005. "On Data Reliability Assessment in Accounting Information Systems," Information Systems Research, INFORMS, vol. 16(3), pages 307-326, September.
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    Cited by:

    1. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    2. Dominikus Kleindienst, 2017. "The data quality improvement plan: deciding on choice and sequence of data quality improvements," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 387-398, November.
    3. Qi Liu & Gengzhong Feng & Giri Kumar Tayi & Jun Tian, 2021. "Managing Data Quality of the Data Warehouse: A Chance-Constrained Programming Approach," Information Systems Frontiers, Springer, vol. 23(2), pages 375-389, April.
    4. Debabrata Dey & Subodha Kumar, 2013. "Data Quality of Query Results with Generalized Selection Conditions," Operations Research, INFORMS, vol. 61(1), pages 17-31, February.

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