1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24615
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHolzer, Severin-
dc.contributor.authorStockinger, Kurt-
dc.date.accessioned2022-03-17T09:31:44Z-
dc.date.available2022-03-17T09:31:44Z-
dc.date.issued2022-03-
dc.identifier.isbn978-3-89318-086-8de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24615-
dc.description.abstractIn this paper we introduce an architecture based on bidirectional recurrent neural networks to detect errors in databases. The experimental results with 6 different datasets demonstrate that our approach outperforms state-of-the-art error detection systems when considering the average of the F1-scores over all datasets. Moreover, our approach achieves a lower standard deviation than existing work, which shows that our system is more robust. Finally, our approach does not require additional data augmentation techniques to achieve high F1-scores.de_CH
dc.language.isoende_CH
dc.publisherOpenProceedingsde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectError detectionde_CH
dc.subjectDatabasede_CH
dc.subjectNeural networkde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDetecting errors in databases with bidirectional recurrent neural networksde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.48786/edbt.2022.22de_CH
dc.identifier.doi10.21256/zhaw-24615-
zhaw.conference.details25th International Conference on Extending Database Technology, Edinburgh (online), 29 March - 1 April 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end367de_CH
zhaw.pages.start364de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of EDBT 2022de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2022_Holzer-Stockinger_Error-Dectection_EDBT.pdfAccepted Version321.86 kBAdobe PDFThumbnail
View/Open
Show simple item record
Holzer, S., & Stockinger, K. (2022). Detecting errors in databases with bidirectional recurrent neural networks [Conference paper]. Proceedings of EDBT 2022, 364–367. https://doi.org/10.48786/edbt.2022.22
Holzer, S. and Stockinger, K. (2022) ‘Detecting errors in databases with bidirectional recurrent neural networks’, in Proceedings of EDBT 2022. OpenProceedings, pp. 364–367. Available at: https://doi.org/10.48786/edbt.2022.22.
S. Holzer and K. Stockinger, “Detecting errors in databases with bidirectional recurrent neural networks,” in Proceedings of EDBT 2022, Mar. 2022, pp. 364–367. doi: 10.48786/edbt.2022.22.
HOLZER, Severin und Kurt STOCKINGER, 2022. Detecting errors in databases with bidirectional recurrent neural networks. In: Proceedings of EDBT 2022. Conference paper. OpenProceedings. März 2022. S. 364–367. ISBN 978-3-89318-086-8
Holzer, Severin, and Kurt Stockinger. 2022. “Detecting Errors in Databases with Bidirectional Recurrent Neural Networks.” Conference paper. In Proceedings of EDBT 2022, 364–67. OpenProceedings. https://doi.org/10.48786/edbt.2022.22.
Holzer, Severin, and Kurt Stockinger. “Detecting Errors in Databases with Bidirectional Recurrent Neural Networks.” Proceedings of EDBT 2022, OpenProceedings, 2022, pp. 364–67, https://doi.org/10.48786/edbt.2022.22.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.