  Lack of data and data quality issues are among the main bottlenecks that
prevent further artificial intelligence adoption within many organizations,
pushing data scientists to spend most of their time cleaning data before being
able to answer analytical questions. Hence, there is a need for more effective
and efficient data cleaning solutions, which, not surprisingly, is rife with
theoretical and engineering problems. This report addresses the problem of
performing holistic data cleaning incrementally, given a fixed rule set and an
evolving categorical relational dataset acquired in sequential batches. To the
best of our knowledge, our contributions compose the first incremental
framework that cleans data (i) independently of user interventions, (ii)
without requiring knowledge about the incoming dataset, such as the number of
classes per attribute, and (iii) holistically, enabling multiple error types to
be repaired simultaneously, and thus avoiding conflicting repairs. Extensive
experiments show that our approach outperforms the competitors with respect to
repair quality, execution time, and memory consumption.
