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  • 标题:Towards Semantic Composition of Geospatial Web Services – Using WSMO in Comparison to BPEL
  • 作者:Moses Gone ; Sven Schade
  • 期刊名称:International Journal of Spatial Data Infrastructures Research
  • 印刷版ISSN:1725-0463
  • 出版年度:2008
  • 卷号:3
  • 期号:3
  • 页码:192-214
  • 出版社:European Commission Joint Research Centre
  • 摘要:Geospatial Web Services (GWS) provide access to geospatial data and expose basic processing functionality using Web Service technology. The complexity of geo-processing tasks necessitates the combination of basic Web Services to user-oriented and application-specific service compositions. Semantic heterogeneity and lack of automation have been identified as core problems that hinder the full exploitation of GWS. Current composition approaches do not address these problems. This paper presents an analysis of the use of Business Process Execution Language (BPEL), a state-of-the-art composition approach. Its major limitations in composing GWSs are highlighted. Semantic Web Services (SWS) composition using the Web Services Modeling Ontology (WSMO) is proposed as improvement. As basis for comparison a use-case application is developed in BPEL and in WSMO.
  • 其他摘要:Geospatial Web Services (GWS) provide access to geospatial data and expose basic processing functionality using Web Service technology. The complexity of geo-processing tasks necessitates the combination of basic Web Services to user-oriented and application-specific service compositions. Semantic heterogeneity and lack of automation have been identified as core problems that hinder the full exploitation of GWS. Current composition approaches do not address these problems. This paper presents an analysis of the use of Business Process Execution Language (BPEL), a state-of-the-art composition approach. Its major limitations in composing GWSs are highlighted. Semantic Web Services (SWS) composition using the Web Services Modeling Ontology (WSMO) is proposed as improvement. As basis for comparison a use-case application is developed in BPEL and in WSMO.
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