22. The method according to claim 1 , wherein there are groupings of data rows and each collection of values contains the values of a single data attribute for a single said grouping of data rows, collections of values corresponding to the same attribute being assigned with the same structure dictionary and the same methods of forming the match granules and structure granules.
22. The method according to claim 1 , wherein there are groupings of data rows and each collection of values contains the values of a single data attribute for a single said grouping of data rows, collections of values corresponding to the same attribute being assigned with the same structure dictionary and the same methods of forming the match granules and structure granules. 23. The method according to claim 22 , wherein scenarios for groupings of data rows include at least one of: each of said groupings contains a single data row; there is a single grouping containing all data rows; each of said groupings contains 65,536 data rows; at least one of the groupings contains a different amount of rows; heuristic optimization algorithms including at least one of: greedy, randomized, and AI-based algorithms, the algorithms evaluating the candidate groupings of data rows based on minimizing the number of collections of values corresponding to the same attributes that include outliers with respect to structures assigned to such attributes; heuristic optimization algorithms including at least one of: greedy, randomized, and AI-based, the algorithms evaluating the candidate groupings of data rows based on a group of criteria including at least one of minimizing the number of distinct structures in collections of values and maximizing the differences between the structure granules corresponding to different collections of values corresponding to the same attributes; and for at least two attributes in a relational data schema, structures in the structure dictionary being assigned to at least one of the two attributes describing semantic relationships of at least one of its values with at least one of the values of the other attribute, heuristic optimization algorithms including at least one of: greedy, randomized, and AI-based algorithms, the algorithms evaluating the candidate groupings of data rows based on at least one of minimizing the number of distinct structures in the collections of values of at least one of the two attributes describing a semantic relationship with the value of other attribute and maximizing the differences between the structure granules corresponding to the collection of values corresponding to the at least one of the two attributes, said structure granules describing the semantic relationship of at least one of its values with at least one of the values of the other attribute.