  Current conditional functional dependencies (CFDs) discovery algorithms
always need a well-prepared training data set. This makes them difficult to be
applied on large datasets which are always in low-quality. To handle the volume
issue of big data, we develop the sampling algorithms to obtain a small
representative training set. For the low-quality issue of big data, we then
design the fault-tolerant rule discovery algorithm and the conflict resolution
algorithm. We also propose parameter selection strategy for CFD discovery
algorithm to ensure its effectiveness. Experimental results demonstrate that
our method could discover effective CFD rules on billion-tuple data within
reasonable time.
