  The analyst effort in data cleaning is gradually shifting away from the
design of hand-written scripts to building and tuning complex pipelines of
automated data cleaning libraries. Hyper-parameter tuning for data cleaning is
very different than hyper-parameter tuning for machine learning since the
pipeline components and objective functions have structure that tuning
algorithms can exploit. This paper proposes a framework, called AlphaClean,
that rethinks parameter tuning for data cleaning pipelines. AlphaClean provides
users with a rich library to define data quality measures with weighted sums of
SQL aggregate queries. AlphaClean applies generate-then-search framework where
each pipelined cleaning operator contributes candidate transformations to a
shared pool. Asynchronously, in separate threads, a search algorithm sequences
them into cleaning pipelines that maximize the user-defined quality measures.
This architecture allows AlphaClean to apply a number of optimizations
including incremental evaluation of the quality measures and learning dynamic
pruning rules to reduce the search space. Our experiments on real and synthetic
benchmarks suggest that AlphaClean finds solutions of up-to 9x higher quality
than naively applying state-of-the-art parameter tuning methods, is
significantly more robust to straggling data cleaning methods and redundancy in
the data cleaning library, and can incorporate state-of-the-art cleaning
systems such as HoloClean as cleaning operators.
