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International Conference on Big Data Analytics and Knowledge Discovery

DaWaK 2015: Big Data Analytics and Knowledge Discovery pp 205–216Cite as

Automatically Tailoring Semantics-Enabled Dimensions for Movement Data Warehouses

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9263))

Abstract

This paper proposes an automatic approach to build tailored dimensions for movement data warehouses based on views of existing hierarchies of objects (and their respective classes) used to semantically annotate movement segments. It selects the objects (classes) that annotate at least a given number of segments of a movement dataset to delineate hierarchy views for deriving tailored analysis dimensions for that movement dataset. Dimensions produced in this way can be quite smaller than the hierarchies from which they are extracted, leading to efficiency gains, among other potential benefits. Results of experiments with tweets semantically enriched with points of interest taken from linked open data collections show the viability of the proposed approach.

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Notes

  1. 1.

    https://dev.twitter.com/rest/public.

  2. 2.

    http://dbpedia.org.

  3. 3.

    http://linkedgeodata.org.

  4. 4.

    http://www.w3.org/TR/vocab-data-cube.

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Acknowledgments

This work was supported by the European Union IRSES-SEEK (grant 295179), CNPq (grant 478634/2011-0), CAPES, and FEESC, the MIUR Project PON ADAPT (no. SCN00447), and by MOTUS (no. MS01 00015 - Industria2015). Special thanks to Cleto May and Douglas Klein for providing semantically enriched data for our experiments.

Author information

Authors and Affiliations

  1. PPGCC/INE/CTC, Federal University of Santa Catarina, Florianópolis, SC, Brazil

    Juarez A. P. Sacenti & Renato Fileto

  2. DAIS, Università Ca’ Foscari Venezia, Venezia, Italy

    Fabio Salvini, Alessandra Raffaetà & Alessandro Roncato

Authors
  1. Juarez A. P. Sacenti
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  2. Fabio Salvini
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  3. Renato Fileto
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  4. Alessandra Raffaetà
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  5. Alessandro Roncato
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Corresponding author

Correspondence to Juarez A. P. Sacenti .

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Editors and Affiliations

  1. University of Science and Technology, Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University, Osaka, Japan

    Takahiro Hara

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Sacenti, J.A.P., Salvini, F., Fileto, R., Raffaetà, A., Roncato, A. (2015). Automatically Tailoring Semantics-Enabled Dimensions for Movement Data Warehouses. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-22729-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22728-3

  • Online ISBN: 978-3-319-22729-0

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