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Defining Spatio-Temporal Granularities for Raster Data

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 6121)

Abstract

The notion of granularity is used in several areas of computing. In temporal databases, granularity relates to the fact that the time frame associated to an event of interest (e.g., an accident) can be envisaged at several levels of detail (e.g., hour, day, month, etc.). Similarly, granularity in data warehousing is the level of detail at which facts (e.g., sales) are captured in dimensions (e.g., product, store, and day). However, there is no commonly-agreed definition of spatial or spatio-temporal granularities. Sometimes, the term spatial granularity is confounded with multiple resolutions. Further, the few proposals about them are mainly focused on the vector data model. In this paper, we define spatial and spatio-temporal granularities for raster data models. In our framework, relations and operations between spatial and spatio-temporal granularities are also defined.

Keywords

  • Space Domain
  • Raster Data
  • Poral Granularity
  • Temporal Granularity
  • Time Granule

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Pozzani, G., Zimányi, E. (2012). Defining Spatio-Temporal Granularities for Raster Data. In: MacKinnon, L.M. (eds) Data Security and Security Data. BNCOD 2010. Lecture Notes in Computer Science, vol 6121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25704-9_10

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  • DOI: https://doi.org/10.1007/978-3-642-25704-9_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25703-2

  • Online ISBN: 978-3-642-25704-9

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