1. A method comprising: retrieving data from an electricity smart grid; encoding said data into a data modeling technique; performing a data quality rules discovery process using a data set of said electricity smart grid and said data modeling technique encoded with said data to said data set to produce a plurality of data quality rules associated with said electricity smart grid, 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.
1. A method comprising: retrieving data from an electricity smart grid; encoding said data into a data modeling technique; performing a data quality rules discovery process using a data set of said electricity smart grid and said data modeling technique encoded with said data to said data set to produce a plurality of data quality rules associated with said electricity smart grid, 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. 5. The method of claim 1 , wherein said predetermined expectation comprises an estimate of how many different data points a candidate conditional functional dependency would apply to.