|Title:||Integrating Geospatial Linked Open Data and Knowledge Networks into Location Business Intelligence||Language:||English||Authors:||Ostrovna, Olha||Qualification level:||Diploma||Keywords:||geospatial; Linked Open Data; Business Intelligence; Location Intelligence; interoperability and reusability||Advisor:||Gartner, Georg||Issue Date:||2020||Number of Pages:||38||Qualification level:||Diploma||Abstract:||
With the development of a breaking theory of Linked Open Data (LOD), many initiatives and projects appear and adopt the LOD concept to geospatial data publishing on the Web. The main idea behind is a provision of integrated access to geospatial data coming fromheterogeneous sources. Such a concept aims to assure convenient reuse and interoperability of location data within various applications and domains. Location Business Intelligence (BI) comes across as one of the interested parties able to transform the potential of geospatial LOD into valuable decisions and solutions. However, the novelty and complexity of the geospatial direction in the LOD concept limit its integration into Location BI. The synthesis of geospatial LOD and Location BI is mutually beneficial and would provide flexibility and uncover new opportunities for users. Hence, this paper identifies and describes the reasons and problems that cause this integration gap. Byconsidering them, both, geospatial LOD providers and BI tools will be able to improve adaptation ,performance and scalability of this valuable amalgamation.The research is based on the exploration and evaluation of geospatial data access and data transfer technologies behind the leaders of the global BI market. Moreover, it examines standards supported by the selected Linked Open Data providers. The approach applied in this paper provides valuable insights on overcoming the existing integration gap between the geospatial Linked Open Data paradigm and Location Business Intelligence solutions.
|DOI:||10.34726/hss.2020.83178||Library ID:||AC16079388||Organisation:||E120 - Department für Geodäsie und Geoinformation||Publication Type:||Thesis
|Appears in Collections:||Thesis|
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checked on Feb 21, 2021
checked on Feb 21, 2021
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