Automatic schema merging using mapping constraints over incomplete sources

Schema merging is the process of consolidating multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autonomous. Clas-sical data integration systems rely on a mediated schema created by human experts through an intensive design process. In this paper, we present a novel approach for merging multi-ple relational data sources related by a collection of mapping con-straints in the form of P2P style tuple-generating dependencies (tgds). In the scenario of data integration, we opt for minimal mediated schemas that are complete regarding certain answers of conjunctive queries. Under Open World Assumption (OWA), we characterize the semantics of schema merging by properties of the output mapping system between the source schemas and the me-diated schema. We propose a merging algorithm following a re-dundancy reduction paradigm and prove that the output satisfies the desired logical properties. Recognizing the fact that multiple plausible mediated schemas may co-exist, a variant of the a priori algorithm is employed to enumerate alternative mediated schemas. Output mappings in the form of data dependencies are generated to support the mediated schemas, which enables query processing. We have evaluated our merging approach over a collection of real world data sets, which demonstrate the applicability and effective-ness of our approach in practice