In paper , conditional functional dependencies (CFDs) are used to detect errors in the data. The basic idea is that CFD makes some assertions on the data. The violations are detected by checking whether the data comply with the CFD. For example, the CFD claims that in a dataset, if an employee is in country “UK” and city “London”, then his/her salary should be higher than “1000”, we then check this CFD on the dataset and find the violations.
Paper  seems to be the first paper talking about CFD.
In paper , a term is added to the commonly used Bayesian technique that represents the probabilistic estimate corresponding to the event that the data is not spurious conditioned upon the data and the true state.
In paper , spatial inconsistency and temporal inconsistency is defined. CFD is also mentioned in this paper. However, this paper is not well written and organized.
A more complete and elegant summary can of spatial and temporal inconsistency can be found in .
Similar as , paper  has a detailed description of CFD on distributed data.
In paper , a likelihood maximization method combined with machine learning is proposed to repair the error data. Similar idea is also discussed in paper .
Paper  provides another idea that inconsistency can be resolved through preferences on data sources.
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