A Probabilistic Decision Model for Entity Matching in Heterogeneous Databases

Debabrata Dey, Sumit Sarkar and Prabuddha De
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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

Management Science, 1998, vol. 44, issue 10, 1379-1395

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.

Keywords: Semantic Heterogeneity; Matching Under Uncertainty; Classification Costs; Assignment Problem (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:44:y:1998:i:10:p:1379-1395

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