Nonparametric Bayesian Modeling for Automated Database Schema Matching
This addresses the schema matching problem for government and commercial applications, but it appears incremental as it builds on existing methods with a specific improvement.
The paper tackles the problem of schema matching for merging databases by introducing a framework that builds nonparametric Bayesian models for each field and compares them based on the probability that a single model generated both fields. The experiments show that this method is more accurate and faster than existing instance-based matching algorithms.
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.