BART in action: Error generation and empirical evaluations of data-cleaning systems

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Repairing erroneous or conicting data that violate a set of constraints is an important problem in data management. Many automatic or semi-automatic data-repairing algorithms have been proposed in the last few years, each with its own strengths and weaknesses. Bart is an open-source error-generation system conceived to support thorough experimental evaluations of these data-repairing systems. The demo is centered around three main lessons. To start, we discuss how generating errors in data is a complex problem, with several facets. We introduce the important notions of detectability and repairability of an error, that stand at the core of Bart. Then, we show how, by changing the features of errors, it is possible to inuence quite significantly the performance of the tools. Finally, we concretely put to work five data-repairing algorithms on dirty data of various kinds generated using Bart, and discuss their performance.

Original languageEnglish (US)
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages2161-2164
Number of pages4
Volume26-June-2016
ISBN (Electronic)9781450335317
DOIs
StatePublished - Jun 26 2016
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: Jun 26 2016Jul 1 2016

Other

Other2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
CountryUnited States
CitySan Francisco
Period6/26/167/1/16

ASJC Scopus subject areas

  • Software
  • Information Systems

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    Santoro, D., Arocena, P. C., Glavic, B., Mecca, G., Miller, R. J., & Papotti, P. (2016). BART in action: Error generation and empirical evaluations of data-cleaning systems. In SIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data (Vol. 26-June-2016, pp. 2161-2164). Association for Computing Machinery. https://doi.org/10.1145/2882903.2899397