Data cube for coverage

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Use Cases

There are multiple Use Cases for using RDF Datacube as a formalism for handling coverage data.

UC1: Describing the content of a coverage.

RDF-QB allows descriptions of the dimensions of coverage. Such descriptions allow coverage data to be discovered and interpreted, and _may_ provide sufficient metadata for certain well-known access methods to be used - e.g. WCS.

RDF-QB dimension descriptions of data support interpretation of data.

It needs to be tested whether this is sufficient for accessing data or if explicit descriptions of supported operations are necessary.

UC2: Describing slices

RDF-QB allows an abstract description of access methods and subsets of a coverage as slices through a set of dimensions

UC3: Encoding coverage data in RDF

The benefits of this need to be established. RDF-QB as a self-description mechanism would implement UC1, which otherwise doesnt imply any particular encoding requirement for the data.

Establishing a Best Practice

Is it sufficient to simply highlight the potential use of RDF-QB and provide informative examples relevant to spatio-temporal data? Are canonical abstract dimensions published as ontologies using RDF-QB (i.e. further specialisations of dimensions for space and time) necessary?