Distributed Discovery of Functional Dependencies

We address the problem of discovering functional dependencies from distributed big data. Existing (non-distributed) algorithms such as FastFDs focus on minimizing computation. However, distributed algorithms must also optimize data communication costs, especially in shared-nothing settings. We propose a distributed version of FastFDs that is communication-efficient and we experimentally show significant performance improvements over a straightforward distributed implementation.