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Spatial Data Integration Using Ontological System

ČERBA, O. Spatial Data Integration Using Ontological System. San Antonio, 2013., ISBN: neuvedeno,
Jazyk publikace: eng
Anglický název: Spatial Data Integration Using Ontological System
Rok vydání: 2013
Místo konání: San Antonio
Název zdroje: ASPRS
Autoři: Ing. Mgr. Otakar Čerba Ph.D. ,
Abstrakt EN: The amount of various spatial data sets is still increasing. But many of existing and emerging spatial data sets represent isolated and self-standing structures without any relation to other data or standards, including data models or nomenclatures. Users quite often prefer to create a new data set instead of re-using existing data. There are many reasons for this situation and the questions connected to spatial data integration and harmonization represent one of the most important task of geomatics and geoinformatics. In this paper, it wants to contribute to a data integration discussions, the approach of spatial data integration focused on semantic aspects of spatial data is introduced. It is based on Extensible Markup Language (XML) family of markup languages group above all on data formats based on XML (GML, KML or proprietary XML data formats), Extensible Stylesheet Language ? Transformation (XSLT) and Resource Descriptor Framework (RDF). The transformation language XSLT is the cornerstone of described procedure. It procures three fundamental processes of spatial data integration ? data sources exploitation, processing (transformation to common data model) and visualization. The implementation of RDF triples and ontological system as the main common data model takes advantage of this approach with regard to complicated non-flat source data structures. Just an integration of heterogeneous data sets represents the typical case of ontological system implementation. As the sample data the collection of various data sets of municipalities and higher administrative unit in the Czech Republic are used. On the one hand these data sets are managed by various organization and institutes and therefore are very heterogeneous. On the other hand citizens and private as well as state organizations do not need isolated data but complex information that could be composed of many different linked data sets.
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