  Real-world datasets are dirty and contain many errors. Examples of these
issues are violations of integrity constraints, duplicates, and inconsistencies
in representing data values and entities. Learning over dirty databases may
result in inaccurate models. Users have to spend a great deal of time and
effort to repair data errors and create a clean database for learning.
Moreover, as the information required to repair these errors is not often
available, there may be numerous possible clean versions for a dirty database.
We propose DLearn, a novel relational learning system that learns directly over
dirty databases effectively and efficiently without any preprocessing. DLearn
leverages database constraints to learn accurate relational models over
inconsistent and heterogeneous data. Its learned models represent patterns over
all possible clean instances of the data in a usable form. Our empirical study
indicates that DLearn learns accurate models over large real-world databases
efficiently.
