The Parameterized Complexity of Dependency Detection in Relational Databases

We study the parameterized complexity of classical problems that arise in the profiling of relational data. Namely, we characterize the complexity of detecting unique column combinations (candidate keys), functional dependencies, and inclusion dependencies with the solution size as parameter. While the discovery of uniques and functional dependencies, respectively, turns out to be W [2]-complete, the detection of inclusion dependencies is one of the first natural problems proven to be complete for the class W [3]. As a side effect, our reductions give insights into the complexity of enumerating all minimal unique column combinations or functional dependencies. 1998 ACM Subject Classification F.2.2 Nonnumerical Algorithms and Problems