Messing up with BART: Error generation for evaluating data-cleaning algorithms

Patricia C. Arocena, Boris Glavic, Giansalvatore Mecca, Renée J. Miller, Paolo Papotti, Donatello Santoro

Research output: Contribution to journalConference articlepeer-review

54 Scopus citations


We study the problem of introducing errors into clean databases for the purpose of benchmarking data-cleaning algorithms. Our goal is to provide users with the highest possible level of control over the error-generation process, and at the same time develop solutions that scale to large databases. We show in the paper that the error-generation problem is surprisingly challenging, and in fact, NP-complete. To provide a scalable solution, we develop a correct and efficient greedy algorithm that srifices completeness, but succeeds under very reasonable assumptions. To scale to millions of tuples, the algorithm relies on several non-trivial optimizations, including a new symmetry property of data quality constraints. The trade-off between control and scalability is the main technical contribution of the paper.

Original languageEnglish (US)
Pages (from-to)36-47
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number2
StatePublished - 2016
Event42nd International Conference on Very Large Data Bases, VLDB 2016 - Delhi, India
Duration: Sep 5 2016Sep 9 2016

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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