In this work we design algorithms for clustering relational columns into attributes, i.e., for identifying strong relationships between columns based on the common properties and characteristics of the values they contain. For example, identifying whether a certain set of columns refers to telephone numbers versus social security numbers, or names of customers versus names of nations. Traditional relational database schema languages use very limited primitive data types and simple foreign key constraints to express relationships between columns. Object oriented schema languages allow the definition of custom data types; still, certain relationships between columns might be unknown at design time or they might appear only in a particular database instance. Nevertheless, these relationships are an invaluable tool for schema matching, and generally for better understanding and working with the data. Here, we introduce data oriented solutions (we do not consider solutions that assume the existence of any external knowledge) that use statistical measures to identify strong relationships between the values of a set of columns. Interpreting the database as a graph where nodes correspond to database columns and edges correspond to column relationships, we decompose the graph into connected components and cluster sets of columns into attributes. To test the quality of our solution, we also provide a comprehensive experimental evaluation using real and synthetic datasets.
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