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If we want to use age as a dimension w/o breaking our pre-aggregation approach, we probably need to apply binning to collapse the integer (fixed precision 1/100th year) into a smaller number of discrete values combined w/ other vocab term concepts to identify a "core_fact".
However, should the bins be uniform or non-uniform intervals? Different bins depending on human or not...?
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