An Ontology-Based Framework for Heterogeneous Data Sources Integration

Ontologies have been extensively used to model domain-specific knowledge. The main reason for this success is due to their capability to be at the “semantic” level, away from data structures and implementation strategies. In addition, ontology formalisms have allowed certain kinds of reasoning to be automated within a reasonable time complexity. Due to ontology data independence and automated reasoning, ontologies are well suited for integrating heterogeneous databases, enabling interoperability among isparate systems, and specifying interfaces to independent, knowledge-based services

A spatial mediator model for integrating heterogeneous spatial data

The complexity and richness of geospatial data create specific problems in heterogeneous data integration. To deal with this type of data integration, we propose a spatial mediator embedded in a large distributed mobile environment (GeoGrid). The spatial mediator takes a user request from a field application and uses the request to select the appropriate data sources, constructs subqueries for the selected data sources, defines the process of combining the results from the subqueries, and develop an integration script that controls the integration process in order to respond to the request. The spatial mediator uses ontologies to support search for both geographic location based on symbolic terms as well as providing a term-based index to spatial data sources based on the relational model. In our approach, application designers only need to be aware of a minimum amount about the queries needed to supply users with the required data. The key part of this research has been the development of the spatial mediator that can dynamically respond to requests within the GeoGrid environment for geographic maps and related relational spatial data