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European Conference on Advances in Databases and Information Systems

ADBIS 2017: New Trends in Databases and Information Systems pp 12–20Cite as

Assessing the Quality of Spatio-Textual Datasets in the Absence of Ground Truth

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 767))

Abstract

The increasing availability of enriched geospatial data has opened up a new domain and enables the development of more sophisticated location-based services and applications. However, this development has also given rise to various data quality problems as it is very hard to verify the data for all real-world entities contained in a dataset. In this paper, we propose ARCI, a relative quality indicator which exploits the vast availability of spatio-textual datasets, to indicate how confident a user can be in the correctness of a given dataset. ARCI operates in the absence of ground truth and aims at computing the relative quality of an input dataset by cross-referencing its entries among various similar datasets. We also present an algorithm for computing ARCI and we evaluate its performance in a preliminary experimental evaluation using real-world datasets.

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Notes

  1. 1.

    http://tour-pedia.org/about/datasets.html.

  2. 2.

    https://developer.foursquare.com.

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Author information

Authors and Affiliations

  1. Masaryk University, Brno, Czech Republic

    Mouzhi Ge

  2. Free University of Bozen-Bolzano, South Tyrol, Italy

    Theodoros Chondrogiannis

Authors
  1. Mouzhi Ge
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  2. Theodoros Chondrogiannis
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Corresponding authors

Correspondence to Mouzhi Ge or Theodoros Chondrogiannis .

Editor information

Editors and Affiliations

  1. Riga Technical University , Riga, Latvia

    Mārīte Kirikova

  2. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  3. University of Cyprus , Nicosia, Cyprus

    George A. Papadopoulos

  4. Free University of Bozen-Bolzano , Bozen-Bolzano, Italy

    Johann Gamper

  5. Institute of Computing Science, Poznan University of Technology, Poznan, Poland

    Robert Wrembel

  6. Université Lumière Lyon 2, Lyon, France

    Jérôme Darmont

  7. University of Bologna , Bologna, Italy

    Stefano Rizzi

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Ge, M., Chondrogiannis, T. (2017). Assessing the Quality of Spatio-Textual Datasets in the Absence of Ground Truth. In: Kirikova, M., et al. New Trends in Databases and Information Systems. ADBIS 2017. Communications in Computer and Information Science, vol 767. Springer, Cham. https://doi.org/10.1007/978-3-319-67162-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-67162-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67161-1

  • Online ISBN: 978-3-319-67162-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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