  Data repair is a common and crucial step in many frameworks today, as
applications may use data from different sources and of different levels of
credibility. Thus, this step has been the focus of many works, proposing
diverse approaches. To assist users in understanding the output of such data
repair algorithms, we propose T-REx, a system for providing data repair
explanations through Shapley values. The system is generic and not specific to
a given repair algorithm or approach: it treats the algorithm as a black box.
Given a specific table cell selected by the user, T-REx employs Shapley values
to explain the significance of each constraint and each table cell in the
repair of the cell of interest. T-REx then ranks the constraints and table
cells according to their importance in the repair of this cell. This
explanation allows users to understand the repair process, as well as to act
based on this knowledge, to modify the most influencing constraints or the
original database.
