Consistent query answers in inconsistent probabilistic databases

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2010, Pages 303-314

  Lian, Xiang ^a   Chen, Lei ^a   Song, Shaoxu ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

all possible repairs semantics; consistent query answering; inconsistent probabilistic database; repair world; repaired possible world

Indexed keywords

ALL-POSSIBLE-REPAIRS SEMANTICS; CONSISTENT QUERY ANSWERING; POSSIBLE WORLDS; PROBABILISTIC DATABASE; REPAIRED POSSIBLE WORLD;

DATABASE SYSTEMS; SEMANTICS;

REPAIR;

EID: 77954736322     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1807167.1807202     Document Type: Conference Paper

References (40)

1. D. J. Abadi, S. Madden, and W. Lindner. REED: Robust, efficient filtering and event detection in sensor networks. In VLDB, 2005.
2. P. Andritsos, A. Fuxman, and R. Miller. Clean answers over dirty databases: A probabilistic approach. In ICDE, 2006.
3. L. Antova, C. Koch, and D. Olteanu. MayBMS: Managing incomplete information with probabilistic world-set decompositions. In ICDE, 2007.
4. M. Arenas, L. Bertossi, and J. Chomicki. Consistent query answers in inconsistent databases. In PODS, 1999.
5. N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger. The R*-tree: an efficient and robust access method for points and rectangles. In SIGMOD, 1990.
6. O. Benjelloun, A. Das Sarma, A. Y. Halevy, and J. Widom. ULDBs: Databases with uncertainty and lineage. In VLDB, 2006.
7. G. Beskales, M. A. Soliman, I. F. Ilyas, and S. Ben-David. Modeling and querying possible repairs in duplicate detection. PVLDB, 2(1), 2009.
8. P. Bohannon, W. Fan, M. Flaster, and R. Rastogi. A cost-based model and effective heuristic for repairing constraints by value modification. In SIGMOD, 2005.
9. J. Boulos, N. N. Dalvi, B. Mandhani, S. Mathur, C. Ré, and D. Suciu. Mystiq: a system for finding more answers by using probabilities. In SIGMOD, 2005.
10. R. Cheng, S. Singh, and S. Prabhakar. U-DBMS: A database system for managing constantly-evolving data. In VLDB, 2005.
11. L. Chiticariu, P. G. Kolaitis, and L. Popa. Interactive generation of integrated schemas. In SIGMOD, 2008.
12. J. Chomicki and J. Marcinkowski. Minimal-change integrity maintenance using tuple deletions. Inf. Comput., 197(1/2), 2005.
13. G. Cong, W. Fan, F. Geerts, X. Jia, and S. Ma. Improving data quality: consistency and accuracy. In VLDB, 2007.
14. G. Cormode, F. Li, and K. Yi. Semantics of ranking queries for probabilistic data and expected ranks. In ICDE, 2009.
15. N. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. VLDB J., 16(4), 2007.
16. X. L. Dong, L. Berti-Equille, and D. Srivastava. Truth discovery and copying detection in a dynamic world. PVLDB, 2(1), 2009.
17. X. L. Dong, A. Halevy, and C. Yu. Data integration with uncertainty. The VLDB Journal, 18(2), 2009.
18. W. Fan. Dependencies revisited for improving data quality. In PODS, 2008.
19. I. Fellegi and D. Holt. A systematic approach to automatic edit and imputation. J. American Statistical Association, 71(353), 1976.
20. A. Fuxman, E. Fazli, and R. Miller. ConQuer: efficient management of inconsistent databases. In SIGMOD, 2005.
21. S. Greco and C. Molinaro. Approximate probabilistic query answering over inconsistent databases. In ER, 2008.
22. M. Hernandez, M. A. Hern Andez, S. Stolfo, and U. Fayyad. Real-world data is dirty: Data cleansing and the merge/purge problem. In Data Min. Knowl. Discov., 1998.
23. M. Hua, J. Pei, W. Zhang, and X. Lin. Ranking queries on uncertain data: a probabilistic threshold approach. In SIGMOD, 2008.
24. Y. W. Huang, N. Jing, and E. A. Rundensteiner. Spatial joins using R-trees: breadth-first traversal with global optimizations. In VLDB, 1997.
25. R. Jampani, F. Xu, M. Wu, L. L. Perez, C. Jermaine, and P. J. Haas. Mcdb: a monte carlo approach to managing uncertain data. In SIGMOD, 2008.
26. R. Kumar, K. Punera, T. Suel, and S. Vassilvitskii. Top-k aggregation using intersections of ranked inputs. In WSDM, 2009.
27. J. Li, B. Saha, and A. Deshpande. A unified approach to ranking in probabilistic databases. PVLDB, 2(1), 2009.
28. L. Mo, Y. He, Y. Liu, J. Zhao, S. Tang, X.-Y. Li, and G. Dai. Canopy closure estimates with greenorbs: Sustainable sensing in the forest. In ACM Sensys, 2009. http://greenorbs.org.
29. T. Neumann, M. Bender, S. Michel, R. Schenkel, P. Triantafillou, and G. Weikum. Distributed top-k aggregation queries at large. Distributed and Parallel Databases, 26(1), 2009.
30. R. A. Pottinger and P. A. Bernstein. Merging models based on given correspondences. In VLDB, 2003.
31. C. Re, N. Dalvi, and D. Suciu. Efficient top-k query evaluation on probabilistic data. In ICDE, 2007.
32. F. Sadri. Reliability of answers to queries in relational databases. TKDE, 3(2), 1991.
33. A. D. Sarma, M. Theobald, and J. Widom. Exploiting lineage for confidence computation in uncertain and probabilistic databases. In ICDE, 2008.
34. M. A. Soliman, I. F. Ilyas, and K. C. Chang. Top-k query processing in uncertain databases. In ICDE, 2007.
35. Y. Tao, V. Hristidis, D. Papadias, and Y. Papakonstantinou. Branch-and-bound processing of ranked queries. Inf. Syst., 32(3), 2007.
36. Y. Theodoridis and T. Sellis. A model for the prediction of R-tree performance. In PODS, 1996.
37. D. Z. Wang, E. Michelakis, M. Garofalakis, and J. Hellerstein. Bayestore: Managing large, uncertain data repositories with probabilistic graphical models. In VLDB, 2008.
38. J. Wijsen. Database repairing using updates. ACM Trans. Database Syst., 30(3), 2005.
39. W. E. Winkler. Methods for evaluating and creating data quality. Inf. Syst., 29(7), 2004.
40. K. Yi, F. Li, G. Kollios, and D. Srivastava. Efficient processing of top-k queries in uncertain databases. In ICDE, 2008.