Unique column combinations (UCCs) are groups of attributes in relational datasets that contain no value-entry more than once. Hence, they indicate keys and serve data management tasks, such as schema normalization, data integration, and data cleansing. Because the unique column combinations of a particular dataset are usually unknown, UCC discovery algorithms have been proposed to find them. All previous such discovery algorithms are, however, inapplicable to datasets of typical real-world size, eg, datasets with more than 50 attributes and a million records.

We present the hybrid discovery algorithm HUCC, which uses the same discovery techniques as the recently proposed functional dependency discovery algorithm HFD: A hybrid combination of fast approximation techniques and efficient validation techniques. With it, the algorithm discovers all minimal unique column combinations in a given dataset. HUCC does not only outperform all existing approaches, it also scales to much larger datasets.