BUNNI: Learning Repair Actions in Rule-driven Data Cleaning

In this work, we address the challenging and open problem of involving non-expert users in the data repairing problem as first-class citizens. Despite a large number of proposals that have been devoted to cleaning data from the point of view of expert users (IT staff and data scientists), there is a lack of studies from the perspective of non-expert ones. Given a set of available data quality rules, we exploit machine learning techniques to guide the user to identify the dirty values for each violation and repair them. We show that with a low user effort, it is possible to identify the values in tuples that can be trusted and the ones that are most likely errors. We show experimentally how this machine learning approach leads to a unique clean solution with high quality in scenarios where other approaches fail.