BART in Action: Error Generation and Empirical Evaluations of Data-Cleaning Systems
| Title | BART in Action: Error Generation and Empirical Evaluations of Data-Cleaning Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Authors | Santoro, Donatello, Arocena, Patricia C., Glavic, Boris, Mecca, Giansalvatore, Miller, Renée J., Papotti, Paolo |
| Conference Name | Proceedings of the 2016 International Conference on Management of Data |
| Date Published | June 2016 |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-3531-7 |
| Keywords | data cleaning, data repairing, empirical evaluation, error generation, pubcrawl170201 |
| Abstract | Repairing erroneous or conflicting 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 influence 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. |
| URL | https://dl.acm.org/doi/10.1145/2882903.2899397 |
| DOI | 10.1145/2882903.2899397 |
| Citation Key | santoro_bart_2016 |