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Acyclic schemes have numerous applications in databases and in machinelearning, such as improved design, more efficient storage, and increasedperformance for queries and machine learning algorithms. Multivalueddependencies (MVDs) are the building blocks of acyclic schemes. The discoveryfrom data of both MVDs and acyclic schemes is more challenging than other formsof data dependencies, such as Functional Dependencies, because thesedependencies do not hold on subsets of data, and because they are verysensitive to noise in the data; for example a single wrong or missing tuple mayinvalidate the schema. In this paper we present Maimon, a system fordiscovering approximate acyclic schemes and MVDs from data. We give aprincipled definition of approximation, by using notions from informationtheory, then describe the two components of Maimon: mining for approximateMVDs, then reconstructing acyclic schemes from approximate MVDs. We conduct anexperimental evaluation of Maimon on 20 real-world datasets, and show that itcan scale up to 1M rows, and up to 30 columns.

Mining Approximate Acyclic Schemes from Relations