Semantic heterogeneity resolution in federated databases by metadata implantation and stepwise evolution

A key aspect of interoperation among data-intensive systems involves the mediation of metadata and ontologies across database boundaries. One way to achieve such mediation between a local database and a remote database is to fold remote metadata into the local metadata, thereby creating a common platform through which information sharing and exchange becomes possible. Schema implantation and semantic evolution, our approach to the metadata folding problem, is a partial database integration scheme in which remote and local (meta)data are integrated in a stepwise manner over time. We introduce metadata implantation and stepwise evolution techniques to interrelate database elements in different databases, and to resolve conflicts on the structure and semantics of database elements (classes, attributes, and individual instances). We employ a semantically rich canonical data model, and an incremental integration and semantic heterogeneity resolution scheme. In our approach, relationships between local and remote information units are determined whenever enough knowledge about their semantics is acquired. The metadata folding problem is solved by implanting remote database elements into the local database, a process that imports remote database elements into the local database environment, hypothesizes the relevance of local and remote classes, and customizes the organization of remote metadata. We have implemented a prototype system and demonstrated its use in an experimental neuroscience environment.