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Functional dependencies (FDs) play an important role in maintaining data quality. They can be used to enforce data consistency and to guide repairs over a database. In this work, we investigate the problem of missing values and its impact on FD discovery. When using existing FD discovery algorithms, some genuine FDs could not be detected precisely due to missing values or some non-genuine FDs can be discovered even though they are caused by missing values with a certain NULL semantics. We define a notion of genuineness and propose algorithms to compute the genuineness score of a discovered FD. This can be used to identify the genuine FDs among the set of all valid dependencies that hold on the data. We evaluate the quality of our method over various real-world and semi-synthetic datasets with extensive experiments. The results show that our method performs well for relatively large FD sets and is able to accurately capture genuine FDs.

Conditional functional dependencies (CFDs) have been recently introduced in the context of data cleaning. They can be seen as an unification of functional dependencies (FDs) and association rules (AR) since they allow to mix attributes and attribute/values in dependencies. In this paper, we introduce our first results on constant CFD inference. Not surprisingly, data mining techniques developed for functional dependencies and association rules can be reused for constant CFD mining. We focus on two types of techniques inherited from FD inference: the first one extends the notion of agree sets and the second one extends the notion of non-redundant sets, closure and quasi-closure. We have implemented the latter technique on which experiments have been carried out showing both the feasibility and the scalability of our proposition.