1. A computer implemented method for classifying at least one source dataset of a computer system, the method comprising: providing a plurality of associated reference tables organized and associated in accordance with a reference storage model in the computer system; calculating, by a data classifier application of the computer system, a first similarity score between the source dataset and a first reference table of the reference tables based on common attributes in the source dataset and a join of the first reference table with at least one further reference table of the reference tables having a relationship with the first reference table, wherein the first similarity score is calculated with the following formula, Score = ∑ dist = 0 n ⁢ ⁢ S dist 1 + dist , where ⁢ ⁢ S dist = ∑ d = 0 ds ⁢ ⁢ 2 * W d N DS + N DT , ds is the number of common attributes, Wd=1/card(D) where card(D) is the cardinality of an attribute D in the reference tables, NDS is the number of attributes in the source dataset, NDT is the number of attributes in the reference tables, n is the number of the at least one further reference table plus the first reference table, and dist is the distance in terms of number of foreign key relationships between the first reference table and the at least one further reference table; and classifying, by the data classifier application, the source dataset by determining using at least the calculated first similarity score whether the source dataset is organized as the first reference table in accordance to the reference storage model.