Sensitivity analysis and explanations for robust query evaluation in probabilistic databases

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2011, Pages 841-852

  Kanagal, Bhargav ^a   Li, Jian ^a   Deshpande, Amol ^a  

^a University of Maryland ^*  (United States)

Author keywords

boolean algebra; lineage; probabilistic databases; query processing; sensitivity analysis

Indexed keywords

BOOLEAN ALGEBRA; DATABASE SYSTEMS; INFORMATION MANAGEMENT; PROBABILITY; SENSITIVITY ANALYSIS;

BOOLEAN FORMULAE; EFFICIENT QUERY EVALUATION; EXISTING SYSTEMS; HIGH PROBABILITY; KEY ISSUES; LINEAGE; PROBABILISTIC DATABASE; QUERY EVALUATION; QUERY RESULTS; UNCERTAIN DATAS;

QUERY PROCESSING;

EID: 79959979272     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1989323.1989411     Document Type: Conference Paper

References (35)

1. L. Antova, T. Jansen, C. Koch, and D. Olteanu. Fast and simple relational processing of uncertain data. In ICDE, 2008.
2. P. Buneman, S. Khanna, and W. C. Tan. Why and where: A characterization of data provenance. In ICDT, pages 316-330, 2001.
3. H. Chan and A. Darwiche. Sensitivity analysis in Markov networks. In IJCAI, pages 1300-1305, 2005.
4. J. Cheney, L. Chiticariu, and W. C. Tan. Provenance in databases: Why, how, and where. Foundations and Trends in Databases, 2009.
5. R. Cheng, J. Chen, and X. Xie. Cleaning uncertain data with quality guarantees. PVLDB, 2008.
6. R. Cheng, D. V. Kalashnikov, and S. Prabhakar. Evaluating probabilistic queries over imprecise data. In SIGMOD, 2003.
7. L. Chiticariu, R. Krishnamurthy, Y. Li, S. Raghavan, F. Reiss, and S. Vaithyanathan. Systemt: An algebraic approach to declarative information extraction. In ACL, 2010.
8. D. G. Corneil, Y. Perl, and L. K. Stewart. A linear recognition algorithm for cographs. SIAM J. Comput., 14(4), 1985.
9. P. Dagum and M. Luby. Approximating probabilistic inference in Bayesian belief networks is np-hard. Artif. Intell., 60(1), 1993.
10. N. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, 2004.
11. M. de Berg, O. Cheong, M. van Kreveld, and M. Overmars. Computational Geometry: Algorithms and Applications (Third Edition). Springer-Verlag, 2008.
12. A. Doan, R. Ramakrishnan, F. C. 0002, P. DeRose, Y. Lee, R. McCann, M. Sayyadian, and W. Shen. Community information management. IEEE Data Eng. Bull., 29(1), 2006.
13. X. L. Dong, A. Y. Halevy, and C. Yu. Data integration with uncertainty. In VLDB, 2007.
14. A. Ferrara, G. Pan, and M. Y. Vardi. Treewidth in verification: Local vs. global. In LPAR, 2005.
15. H. Garcia-Molina. Entity resolution: Overview and challenges. In ER, pages 1-2, 2004.
16. M. C. Golumbic, A. Mintz, and U. Rotics. Factoring and recognition of read-once functions using cographs and normality. In DAC, 2001.
17. T. J. Green, G. Karvounarakis, and V. Tannen. Provenance semirings. In PODS, pages 31-40, 2007.
18. R. Gupta and S. Sarawagi. Creating probabilistic databases from information extraction models. In VLDB, 2006.
19. J. Y. Halpern and J. Pearl. Causes and explanations: A structural-model approach - part ii: Explanations. In IJCAI, 2001.
20. J. Y. Halpern and J. Pearl. Causes and explanations: A structural-model approach: Part 1: Causes. In UAI, 2001.
21. R. Jampani, F. Xu, M. Wu, L. L. Perez, C. M. Jermaine, and P. J. Haas. Mcdb: a monte carlo approach to managing uncertain data. In SIGMOD, 2008.
22. T. S. Jayram, S. Kale, and E. Vee. Efficient aggregation algorithms for probabilistic data. In SODA, pages 346-355, 2007.
23. T. S. Jayram, R. Krishnamurthy, S. Raghavan, S. Vaithyanathan, and H. Zhu. Avatar information extraction system. IEEE Data Eng. Bull., 2006.
24. B. Kanagal and A. Deshpande. Lineage processing over correlated probabilistic databases. In SIGMOD, 2010.
25. U. Kjærulff and L. C. van der Gaag. Making sensitivity analysis computationally efficient. In UAI, 2000.
26. A. Krause and C. Guestrin. Optimal nonmyopic value of information in graphical models - efficient algorithms and theoretical limits. In IJCAI, 2005.
27. A. Meliou, W. Gatterbauer, K. F. Moore, and D. Suciu. The complexity of causality and responsibility for query answers and non-answers. In PVLDB, 2011.
28. D. Olteanu, J. Huang, and C. Koch. Approximate confidence computation in probabilistic databases. In ICDE, 2010.
29. C. Re, N. N. Dalvi, and D. Suciu. Efficient top-k query evaluation on probabilistic data. In ICDE, pages 886-895, 2007.
30. C. Ré and D. Suciu. Approximate lineage for probabilistic databases. PVLDB, 2008.
31. A. D. Sarma, O. Benjelloun, A. Y. Halevy, and J. Widom. Working models for uncertain data. In ICDE, 2006.
32. P. Sen, A. Deshpande, and L. Getoor. Prdb: managing and exploiting rich correlations in probabilistic databases. VLDB J., 18(5):1065-1090, 2009.
33. P. Sen, A. Deshpande, and L. Getoor. Read-once functions and query evaluation in probabilistic databases. In PVLDB, 2010.
34. P. Senellart and G. Gottlob. On the complexity of deriving schema mappings from database instances. In PODS, pages 23-32, 2008.
35. J. Widom. Trio: A system for integrated management of data, accuracy, and lineage. In CIDR, 2005.