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

Integrating GIS and Imagery through XML-based Information Mediation

this paper, we propose a mediation-based approach for integrating information from two types of information sources, viz. spatial information systems such as GIS and searchable databases of geo-referenced imagery. As in [14,19], our goal is to enable users to issue a single query in order to search multiple information sources and, in return, receive a combined result incorporating data from across these sources. Similarly, we would like to provide authenticated and authorized users the ability to update sources. This paper describes the architecture of a mediation-based system and steps through the query evaluation procedure in an such a system. We emphasize that the notion of "integration" addressed in this paper does not rest on the development of spatial algorithms that operate on images or vector data and achieve "physical integration" (e.g., see [11]) through techniques like image conflation, as described in, say, [11]. Instead, we aim to attain "logical integration" by creating correspondences between related spatial information similar to non-spatial mediation systems [10]. We demonstrate how existing physical integration techniques can fit into our information association methodology. However, the development of such methods is not the focus of this paper