I have created some linked data statistical datasets though, and tried
The verbosity of RDF for representing multi-dimensional data makes me
> * If the data is available as Linked Data, then is there an expectation that
think that RDF stores might not be the most efficient way of storing
and querying large volumes of stats, so I think expectation of a
SPARQL endpoint might be lower (though it's possible something like
D2RQ might work better?).
When (at Talis) we had to produce a 'triplification' of the European
> * Is a SPARQL endpoint more important than Linked Data (e.g. because toolsbasic Linked Data - dereffing CBDs of all the observations for
> need to perform arbitrary queries, rather than follow-your-nose traversal)
> * Might the Linked Data API offer a means for exposing richer access to
> data, without (direct) recourse to a SPARQL endpoint
instance, could be pretty tiresome.
SPARQL is more efficient in that regard, but it would be nice to
combine the fyn discovery of linked data, and SPARQL's ability to
slice in different ways and calculate aggregates.
Linked Data API can let you provide these different views, but, for
statistical data, it would be nice to be able to also have calculated
properties - the average, max, min values for a slice (for instance) -
the areas dimension with the highest/lowest value for a
measureproperty and time dimension - that kind of thing.
At root, statistics are interesting because they let you compare
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