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International Conference on Business Information Systems

BIS 2018: Business Information Systems Workshops pp 566–578Cite as

Situation-Dependent Data Quality Analysis for Geospatial Data Using Semantic Technologies

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 339))

Abstract

In this paper we present a new way to evaluate geospatial data quality using Semantic technologies. In contrast to non-semantic approaches to evaluate data quality, Semantic technologies allow us to model situations in which geospatial data may be used and to apply costumized geospatial data quality models using reasoning algorithms on a broad scale. We explain how to model data quality using common vocabularies of ontologies in various contexts, apply data quality results using reasoning in a real-world application case using OpenStreetMap as our data source and highlight the results of our findings on the example of disaster management planning for rescue forces. We contribute to the Semantic Web community and the OpenStreetMap community by proposing a semantic framework to combine usecase dependent data quality assignments which can be used as reasoning rules and as data quality assurance tools for both communities respectively.

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Acknowledgements

This work was funded by the German Federal Ministry of Education and Research under project reference number 03FH032IX4.

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Authors and Affiliations

  1. Mainz University of Applied Sciences, Lucy-Hillebrand-Straße 2, 55128, Mainz, Germany

    Timo Homburg & Frank Boochs

Authors
  1. Timo Homburg
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  2. Frank Boochs
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Correspondence to Timo Homburg .

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Editors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Witold Abramowicz

  2. Fraunhofer FOKUS, Berlin, Berlin, Germany

    Adrian Paschke

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Homburg, T., Boochs, F. (2019). Situation-Dependent Data Quality Analysis for Geospatial Data Using Semantic Technologies. In: Abramowicz, W., Paschke, A. (eds) Business Information Systems Workshops. BIS 2018. Lecture Notes in Business Information Processing, vol 339. Springer, Cham. https://doi.org/10.1007/978-3-030-04849-5_49

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  • DOI: https://doi.org/10.1007/978-3-030-04849-5_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04848-8

  • Online ISBN: 978-3-030-04849-5

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