Multigranular spatio-temporal models: implementation challenges
Multiple granularities provide an essential support for extracting significant knowledge from spatio-temporal datasets at different levels of details. They enable to zoom-in and zoom-out spatio-temporal datasets, thus enhancing the data modelling exibility and improving the analysis of information. In this paper we investigate the implementation issues arising when a data model and a query language are enriched with spatio-temporal multigranularity. We introduce appropriate representations for space and time dimensions, granularities, granules, and multi-granular values. Finally, we discuss how multigranular spatio-temporal conversions affect data usability and how such important property may be guaranteed.
Logical design, data models, spatial databases
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