I think those are both good examples of where you can manage the cost of a false positive.
In genomics, we're using this to map a DNA substring (or "k-mer") to a value. We can tolerate a very low error rate for those individual substrings, especially since any erroneous values will be random (vs. having the same or correlated values). So, with some simple threshold-based filtering, our false positive problem goes away.
Again, you'll never get the incorrect value for a key in the B-field, only for a key not in the B-field (which can return a false positive with a low, tunable error rate).
In genomics, we're using this to map a DNA substring (or "k-mer") to a value. We can tolerate a very low error rate for those individual substrings, especially since any erroneous values will be random (vs. having the same or correlated values). So, with some simple threshold-based filtering, our false positive problem goes away.
Again, you'll never get the incorrect value for a key in the B-field, only for a key not in the B-field (which can return a false positive with a low, tunable error rate).