Don't be SCAREd: Use SCalable Automatic REpairing with maximal likelihood and bounded changes

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2013, Pages 553-564

  Yakout, Mohamed ^a   Berti Équille, Laure ^b   Elmagarmid, Ahmed K ^c  

^a MICROSOFT   (United States)

^b INSTITUT DE RECHERCHE POUR LE DÉVELOPPEMENT   (France)

^c QATAR COMPUTING RESEARCH INSTITUTE   (Qatar)

Author keywords

Data cleaning; Inconsistent data

Indexed keywords

AUTOMATIC REPAIRING; COMPUTATIONAL PROCEDURES; DATA CLEANING; INCONSISTENT DATA; LIKELIHOOD METHODS; MACHINE LEARNING TECHNIQUES; REAL-WORLD DATASETS; STATISTICAL MACHINE LEARNING;

ERRORS; FORECASTING; LEARNING SYSTEMS; SCALABILITY;

REPAIR;

EID: 84880515658     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2463676.2463706     Document Type: Conference Paper

References (23)

1. S. Arora, D. Karger, and M. Karpinski. Polynomial time approximation schemes for dense instances of np-hard problems. In STOC, 1995.
2. Y. Asahiro, K. Iwama, H. Tamaki, and T. Tokuyama. Greedily finding a dense subgraph. In Journal of Algorithms, 2000.
3. G. Cong, W. Fan, F. Geerts, X. Jia, and S. Ma. Improving data quality: consistency and accuracy. In VLDB, 2007.
4. K. Dembczynski, W. Cheng, and E. Hullermeier. Bayes optimal multilabel classification via probabilistic classifier chains. In ICML, 2010.
5. T. G. Dietterich. Ensemble methods in machine learning. In MCS workshop, 2000.
6. A. K. Elmagarmid, P. G. Ipeirotis, and V. S. Verykios. Duplicate record detection: A survey. TKDE, 2007.
7. L. P. English. Information quality applied: best practices for improving business information, processes and systems. Wiley, 2009.
8. W. Fan. Dependencies revisited for improving data quality. In PODS, 2008.
9. W. Fan, F. Geerts, L. V. Lakshmanan, and M. Xiong. Discovering conditional functional dependencies. In ICDE, 2009.
10. W. Fan, J. Li, S. Ma, N. Tang, and W. Yu. Towards certain fixes with editing rules and master data. PVLDB, 2010.
11. W. Fan, J. Li, S. Ma, N. Tang, and W. Yu. Interaction between record matching and data repairing. In SIGMOD, 2011.
12. U. Feige and M. Seltser. On the densest k-subgraph problem. Algorithmica, 1997.
13. S. German and D. German. Neurocomputing: foundations of research. chapter Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. 1988.
14. D. Heckerman, D. M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for inference, collaborative filtering, and data visualization. J. Mach. Learn. Res., 2001.
15. D. S. Hochbaum. Efficient bounds for the stable set, vertex cover and set packing problems. In Discrete Applied Mathematics, 1983.
16. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptive mixtures of local experts. Neural Comput.
17. J. L. Y. Koh, M. L. Lee, W. Hsu, and K. T. Lam. Correlation-based detection of attribute outliers. In DASFAA, 2007.
18. S. Kolahi and L. V. S. Lakshmanan. On approximating optimum repairs for functional dependency violations. In ICDT, 2009.
19. A. Lopatenko and L. Bravo. Efficient approximation algorithms for repairing inconsistent databases. In ICDE, 2007.
20. C. Mayfield, J. Neville, and S. Prabhakar. Eracer: a database approach for statistical inference and data cleaning. In SIGMOD, 2010.
21. S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. J. Mach. Learn. Res., 2006.
22. M. Yakout, A. K. Elmagarmid, J. Neville, M. Ouzzani, and I. F. Ilyas. Guided data repair. PVLDB, 2011.
23. X. Zhu and X. Wu. Class noise vs. attribute noise: a quantitative study of their impacts. Artif. Intell. Rev., 22:177-210, November 2004.