9. A method comprising: retrieving data about a health care domain; encoding said data into a data modeling technique; performing a data quality rules discovery process using a data set of said health care domain and said data modeling technique encoded with said data to said data set to produce a plurality of data quality rules for said data set associated with said health care domain, said data quality rules discovery process comprising: applying each of a set of candidate conditional functional dependencies to a data segment having a predetermined length of data points of said data set; and after said applying, if a candidate conditional functional dependency has a result signature that does not meet a predetermined expectation, refining that candidate conditional functional dependency by eliminating an attribute in that candidate conditional functional dependency that took on the most values throughout the data set; and performing an analysis on said plurality of data quality rules to generate a derivative data quality rule for said data set.
14. The method of claim 9 , wherein said predetermined expectation comprises an estimate of how many different data points a candidate conditional functional dependency would apply to.