The basic recursive exhaustion algorithm is applicable to expanding the amount of information available for individuals. Using only public domain information about you, tens, hundreds, or thousands of fields, this algorithm could expand the amount to millions, billions or more fields. This is not an exaggeration.
The same recursive exhaustion algorithm could be applied to the transpose of the original data matrix. In spreadsheet terms, create a new table whose records are fields of the old one, and fields are records from the old one. Some spreadsheet software makes it easy to do this.
Recursive exhaustion can also be applied to this transposed data matrix. Doing so would be expanding the number of individuals for which data the records apply. For example, one record might represent a birth year, another a birth month, and the third a birth day.
Looking at the record for a birth year, this inverted form of recursive exhaustion would increase the number people apparently born in that year. Note that sometimes this would specify a point in the vector space which is not occupied by any real person.
In attempting to decode the mathematical back to the original human-readable format, the chances it does describe a real person could be obtained, along with the nearest match. Finding the nearest match could help to correct the original data. In some cases a mythical figure could be added as a set of fields in an attribute record.
The results of this could help determine which individuals are purely or mostly mythical. Real or not, data about them could be pinned down. Did the demigod Hercules (Heracles) have any real (human) existence, or was he purely mythical?
Whether he existed or not, examination of the data records could provide a birthdate or most probable birthdate for him.
As in many cases, the best results obtainable using recursive exhaustion would involve alternating between the original and transposed data matrices.