Statistical Validation of Column Matching in the Database Schema Evolution of the Brazilian Public School Census

Publicly available datasets are subject to new versions, with each version potentially reflecting changes to the data. These changes may involve adding or removing attributes, changing data types, and modifying values or their semantics. Integrating these datasets into a relational database poses a significant challenge: How to keep track of the evolving database schema while incorporating different versions of the data sources? This paper presents a statistical methodology to validate the integration of 12 years of open access datasets from Brazil’s School Census, with a new version of the datasets released annually by the Brazilian Ministry of Education (MEC). We employ various statistical tests to find matching attributes between datasets from a specific year and their potential equivalents in datasets from later years. The results show that using the Kolmogorov–Smirnov test, we can successfully match columns from different dataset versions in about 90% of cases.