Abstract
Extracting minimal functional dependencies (MFDs) from relational databases is an import database analysis technique. With the advent of big data era, it is challenging to discover MFDs from big data, especially large-scale distributed data stored in many different sites. The key to discovering MFDs as fast as possible is pruning the useless candidate MFDs. And in most existed algorithms, it usually prunes candidate MFDs from top to bottom or from bottom to top. We present a new algorithms FastMFDs for discovering all MFDs from large-scale distributed data both from top to bottom and from bottom to top in parallel. We experimented our algorithm in real-life datasets, and our algorithm is more efficient and faster than the existed discovering algorithms.