A data integration system provides the user with a unified view, called global schema, of the data residing at different sources. Users issue their queries against the global schema, and the system computes answers to queries by suitably accessing the sources, through the mapping, i.e., the specification of the relationship between the global schema and the sources. Since sources are in general...

Intuitively, data management and data integration tools should be well-suited for exchanging information in a semantically meaningful way. Unfortunately, they suffer from two significant problems: they typically require a comprehensive schema design before they can be used to store or share information, and they are difficult to extend because schema evolution is heavyweight and may break backw...

Today’s fast and continuous growth of large business organizations enforces an increasing need in integrating and sharing large amounts of data, coming from a number of heterogeneous and distributed data sources. Thus, during the last decade, research and business community interest has migrated from DataBase Management Systems (DBMS) to data integration systems. Whereas the former make a uniqu...

This paper presents a detailed integration process for XML schemata called BInXS. BInXS adopts a global-as-view integration approach that builds a global schema from a set of heterogeneous XML schemata related to a same application domain. This bottom-up approach maps all element and attribute definitions in XML schemata to correspondent concepts at the global schema, allowing access to all dat...

Currently, there are two main basic approaches to data integration: Global-as-View (GaV) and Local-as-View (LaV). However, both GaV and LaV have their limitations. In a GaV approach, changes in information sources or adding a new information source requires revisions of a global schema and mappings between the global schema and source schemas. In a LaV approach, automating query reformulation h...

Data integration involves merging two or more schemas into a global schema based on the identification of common concepts, and also the set of different concepts that are mutually related by some semantic properties. Identifying semantic relationships that exist between a set of objects in one schema and a different set of objects in another schema is crucial to the integration process. Earlier...

AbstrAct: Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, " given two schemas, S i and S j , find the most plausible correspondences between the elements of S i and S j , exploiting all available information, such as the schemas, instance data, and auxiliary sources " [24]. Given the ...

The goal of data integration is to provide a uniform access to a set of heterogeneous data sources, freeing the user from the knowledge about where the data are, how they are stored, and how they can be accessed. One of the outcomes of the research work carried out on data integration in the last years is a clear conceptual architecture, comprising a global schema, the source schema, and the ma...

Both the federated approach and the mediator approach to database integration require that semantic diierences between schemas be resolved by a process of schema transformation. Data, queries and updates subsequently passed between schemas need to be transformed accordingly. We have developed a general framework to support the schema transformation process. This framework consists of a graph-ba...