  We introduce HoloClean, a framework for holistic data repairing driven by
probabilistic inference. HoloClean unifies existing qualitative data repairing
approaches, which rely on integrity constraints or external data sources, with
quantitative data repairing methods, which leverage statistical properties of
the input data. Given an inconsistent dataset as input, HoloClean automatically
generates a probabilistic program that performs data repairing. Inspired by
recent theoretical advances in probabilistic inference, we introduce a series
of optimizations which ensure that inference over HoloClean's probabilistic
model scales to instances with millions of tuples. We show that HoloClean
scales to instances with millions of tuples and find data repairs with an
average precision of ~90% and an average recall of above ~76% across a diverse
array of datasets exhibiting different types of errors. This yields an average
F1 improvement of more than 2x against state-of-the-art methods.
