That's where we aren't sure what to do really. There are three values that correspond to an attribute. Lets say X,Y, and Z. There are a series of qualifiers that have to be met: Lets say Q1, Q2, and Q3. So Q1 will be X has to be greater than Y. Q2 will be X-Y has to be greater than Z. And Q3 will be (X*Y)/Z needs to be greater than (X-Y)*Z. All three of these conditions need to be met in order to say the attribute is present. A binary value of 1.

I just made those up, so they may not even make sense. But it should illustrate the point.

Then each group of data I am interested in looking at has a million attributes, and I'm interested in comparing 200 or so of these groups at a time. So I have to either incorporate those qualifiers in a select statement of some sort, or incorporate them into a script that manipulates those raw database values after they are selected. **It would be pertinent to note that the groups of 200 change, and so do the qualifiers.

Once the binary data sets are created, then those groups have to be evaluated by their assigned category. (A static value assigned to the dataset in the database.) That evaluation is the other part of my question in this thread. How to group the categories, and look for unique attributes.

Again, the database doesn't exist yet, so I'm working from tab delimited binary datasets where I have already processed the qualifiers.