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.

Automatically tailoring semantics-enabled dimensions for movement data warehouses

RAFFAETA', Alessandra;RONCATO, Alessandro
2015

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.
Big Data Analytics and Knowledge Discovery
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3662227
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