Nonparametric Bayesian Modeling for Automated Database Schema Matching

Ferragut, Erik M; Laska, Jason A

doi:10.1109/icmla.2015.235

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Nonparametric Bayesian Modeling for Automated Database Schema Matching

Ferragut, Erik M·Laska, Jason A

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Abstract

The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.

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Ferragut, Erik M, Laska, Jason A. 2015-01-01. Nonparametric Bayesian Modeling for Automated Database Schema Matching. https://doi.org/10.1109/icmla.2015.235

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