Divide & Conquer-based Inclusion Dependency Discovery

The discovery of all inclusion dependencies (INDs) in a dataset is an important part of any data profiling effort. Apart from the detection of foreign key relationships, INDs can help to perform data integration, query optimization, integrity checking, or schema (re-)design. However, the detection of INDs gets harder as datasets become larger in terms of number of tuples as well as attributes. To this end, we propose Binder, an IND detection system that is capable of detecting both unary and n-ary INDs. It is based on a divide & conquer approach, which allows to handle very large datasets – an important property on the face of the ever increasing size of today’s data. In contrast to most related works, we do not rely on existing database functionality nor assume that inspected datasets fit into main memory. This renders Binder an efficient and scalable competitor. Our exhaustive experimental evaluation shows the high superiority of Binder over the state-of-the-art in both unary (Spider) and n-ary (Mind) IND discovery. Binder is up to 26x faster than Spider and more than 2500x faster than Mind.