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Identification and adjustment of corresponding objects in data sets of different origin

The paper tackles the problem of data integration. It presents first results of an approach for the instance-based semantic integration. Subsequently, the geometric integration is presented using a method for homogenization that allows for a partial integration taking different geometric deviations into account. Results are shown with the example of the integration and adaptation of data of different origin from topographic and navigation data sets. Introduction and overview The availability of spatial data sets, especially also over the internet via web services, allows the combination of data of different origin. However, geometric overlay of data often reveals semantic and geometric differences - even when the same or similar objects in reality are concerned. In order to be able to have an integrated analysis, the data sets have to be fused or homogenized. The fusion process needs some basic prerequisites, which will be tackled in the paper: first of all, semantically corresponding objects have to be known. Then, instances of these objects have to be identified which can be overlaid. Finally, the boundaries have to be mutually adjusted in order to obtain one synchronized object geometry. Based on a set of corresponding objects which serve as "ground control objects" or anchor objects, also the rest of the data set, i.e. the non-anchor objects can be transformed. Having such a system realizes the vision that data sets can be selected and loaded for a certain application, followed by an automatic mutual adjustment, so that a coherent geometry is determined as a basis for follow-up analysis and visualization processes. In the ideal case this is done on-the-fly. The adaptation can be considered as a kind of synchronization process. For this synchronization rules have to be defined that specify how objects have to be altered in the fusion process. Thus these rules control e.g. whether one object is representing a "master geometry" to which the objects of the other data set are adjusted, or alternatively, whether an intermediate geometry between both data sets has to be determined. The paper is organized as follows. In the next section the background of the research is sketched as well as references to existing work are given. Then, the first results of the semantic integration is described. Subsequently, our method for the geometric adaptation is shown. A summary and an outlook conclude the paper.