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Answer to question #10:
Not sure if the problem of fuzzy merging can be addressed within the framework of traditional databases. Say you have a table A with 10,000 users (key is user ID), a table B with 50,000 users (key is user ID). You could created a user mapping table C with three fields:
userID (= key),
Alternate_UserID (this field would also be a user ID) and
Probability (probability that userID = Alternate_UserID).
This table would be populated after some machine learning algorithm had been applied to tables A and B to identify similar users and the probability they match. Make sure that you only include (in table C) records where probability is above (say) 0.25, otherwise you risk exploding your database.