BMatch: A Quality/Performance Balanced Approach for Large Scale Schema Matching

Schema matching is a crucial task to gather information of the same domain. This is even more the case when dealing with data warehouses, where a large number of data sources are available and require matching and integration. However, the matching process is still largely performed manually or semiautomatically, thus discouraging the use of large scale integration systems. Indeed, these large scale scenarios require a solution which ensures both an acceptable matching quality and good performance. In this article, we present an approach to efficiently match a large number of schemas. The quality aspect is based on the combination of terminological methods and cosine measure between context vectors. The performance aspect relies on a B-tree indexing structure to reduce the search space. Finally, our approach has been implemented and experiments with real sets of schemas show that it is both scalable and provides an acceptable quality of matches as compared to results obtained by the most referenced schema matching tools.