Building the index: example with more than one categorical dimension
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I recently came across the nanocubes paper and would like to understand better the process for building the index.
The paper contains a worked example with one spatial dimension, one categorical dimension and a temporal dimension.
From what I can understand, the indexing process involves building nested tree data structures for the spatial and categorical dimensions with the final temporal dimension using the summed-area table variant, with memory efficiencies gained by sharing subtrees (and template based container specialization, based on the cardinality of each dimension).
Would I be correct in thinking that, for two categorical dimensions, the flat-tree representing the first categorical dimension would simply point to further flat-trees representing the second categorical dimension?
Is there an example anyone could share, similar to that given in section 4.2 of the paper, which shows how the index is built in this case?