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New Similarity Measure for Spatio-Temporal OLAP Queries

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

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Abstract

Storing, querying, and analyzing spatio-temporal data are becoming increasingly important, as the availability of volumes of spatio-temporal data increases. One important class of spatio-temporal analysis is computing spatio-temporal queries similarity. In this paper, we focus on assessing the similarity between Spatio-Temporal OLAP queries in term of their GeoMDX queries. However, the problem of measuring Spatio-Temporal OLAP queries similarity has not been studied so far.Therefore, we aim at filling this gap by proposing a novel similarity measure. The proposed measure can be used either in developing query recommendation, personalization systems or speeding-up query evolution. It takes into account the temporal similarity and the basic components of spatial similarity assessment relationships.

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Author information

Authors and Affiliations

  1. Computer Science Department, BESTMOD Laboratory, Institut Supérieur de Gestion de Tunis, Tunis, Tunisia

    Olfa Layouni & Jalel Akaichi

Authors
  1. Olfa Layouni
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  2. Jalel Akaichi
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Correspondence to Olfa Layouni .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Layouni, O., Akaichi, J. (2016). New Similarity Measure for Spatio-Temporal OLAP Queries. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_26

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34098-2

  • Online ISBN: 978-3-319-34099-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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