1 |
Chu, X., Ilyas, I. F., Krishnan, S., and Wang, J. (2016). Data cleaning: Overview and emerging challenges. In SIGMOD, page 2201–2206.
|
|
2 |
Dallachiesa, M., Ebaid, A., Eldawy, A., Elmagarmid, A., Ilyas, I. F., Ouzzani, M., and Tang, N. (2013). Nadeef: a commodity data cleaning system. In Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, SIGMOD ’13, page 541–552, New York, NY, USA. Association for Computing Machinery.
|
|
3 |
Fan, W., Geerts, F., Jia, X., and Kementsietsidis, A. (2008). Conditional functional de- pendencies for capturing data inconsistencies. ACM Trans. Database Syst., 33(2).
|
|
4 |
Kersten, T., Leis, V., Kemper, A., Neumann, T., Pavlo, A., and Boncz, P. (2018). Ev- erything you always wanted to know about compiled and vectorized queries but were afraid to ask. Proc. VLDB Endow., 11(13):2209–2222.
|
|
5 |
Livshits, E., Kochirgan, R., Tsur, S., Ilyas, I. F., Kimelfeld, B., and Roy, S. (2021). Proper- ties of inconsistency measures for databases. In Proceedings of the 2021 International Conference on Management of Data, SIGMOD ’21, page 1182–1194, New York, NY, USA. Association for Computing Machinery.
|
|
6 |
Neumann, T. and Freitag, M. J. (2020). Umbra: A disk-based system with in-memory per- formance. In 10th Conference on Innovative Data Systems Research, CIDR 2020, Am- sterdam, The Netherlands, January 12-15, 2020, Online Proceedings. www.cidrdb.org.
|
|
7 |
Pena, E. H. M., de Almeida, E. C., and Naumann, F. (2021). Fast detection of denial constraint violations. Proc. VLDB Endow., 15(4):859–871.
|
|
8 |
Pena, E. H. M., Porto, F., and Naumann, F. (2022). Fast algorithms for denial constraint discovery. Proc. VLDB Endow., 16(4):684–696.
|
|
9 |
Raasveldt, M. and Mu ̈hleisen, H. (2019). Duckdb: an embeddable analytical database. In Proceedings of the 2019 International Conference on Management of Data, SIGMOD ’19, page 1981–1984, New York, NY, USA. Association for Computing Machinery.
|
|
10 |
Rekatsinas, T., Chu, X., Ilyas, I. F., and Re ́, C. (2017). HoloClean: Holistic data repairs with probabilistic inference. Proc. VLDB Endow., 10(11):1190–1201.
|
|