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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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.
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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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