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A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases

Author

Listed:
  • Debabrata Dey

    (Department of Management Science, School of Business Administration, Box 353200, University of Washington, Seattle, Washington 98195-3200)

  • Sumit Sarkar

    (Department of Management Science and Information Systems, School of Management, University of Texas at Dallas, Richardson, Texas 75803-0688)

  • Prabuddha De

    (Department of MIS and Decision Sciences, School of Business Administration, University of Dayton, Dayton, Ohio 45469-2130)

Abstract

In recent years, there has been a proliferation of database systems in all types of organizations. In many cases, these databases are developed in different departments and maintained autonomously. Much is to be gained, however, if databases across departments, divisions, or even organizations can be related to one another. One main problem of relating data stored in different databases is the differences in their representation of real-world entities, such as the use of different identifiers or primary keys. We present a decision theoretic model for matching entities across different databases. The decision to match two entities from two different databases inherently involves some uncertainty since an exact match may not be found because of errors in data collection, data entry, and data representation. We model this uncertainty using probability theory and propose an integer programming formulation that minimizes the total cost associated with the entity matching decision. The model has been implemented and validated on real-world data.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:10:p:1379-1395
    DOI: 10.1287/mnsc.44.10.1379
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    References listed on IDEAS

    as
    1. J. B. Copas & F. J. Hilton, 1990. "Record Linkage: Statistical Models for Matching Computer Records," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(3), pages 287-312, May.
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    Cited by:

    1. Kartik Hosanagar, 2011. "Usercentric Operational Decision Making in Distributed Information Retrieval," Information Systems Research, INFORMS, vol. 22(4), pages 739-755, December.
    2. 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.
    3. Debabrata Dey, 2003. "Record Matching in Data Warehouses: A Decision Model for Data Consolidation," Operations Research, INFORMS, vol. 51(2), pages 240-254, April.
    4. Jiexun Li & G. Alan Wang & Hsinchun Chen, 2011. "Identity matching using personal and social identity features," Information Systems Frontiers, Springer, vol. 13(1), pages 101-113, March.
    5. Shaobo Li & Matthew J. Schneider & Yan Yu & Sachin Gupta, 2023. "Reidentification Risk in Panel Data: Protecting for k -Anonymity," Information Systems Research, INFORMS, vol. 34(3), pages 1066-1088, September.
    6. 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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