Bridging the gap between intensional and extensional query evaluation in probabilistic databases

Advances in Database Technology - EDBT 2010 - 13th International Conference on Extending Database Technology, Proceedings

Volumn , Issue , 2010, Pages 323-334

  Jha, Abhay ^a   Olteanu, Dan ^b   Suciu, Dan ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Probabilistic databases; Query processing

Indexed keywords

EVALUATION STRATEGIES; POST PROCESSING; PROBABILISTIC DATABASE; PROBABILISTIC DATABASES; PROBABILISTIC INFERENCE; QUERY EVALUATION;

QUERY PROCESSING; TECHNOLOGY; UNCERTAINTY ANALYSIS;

DATABASE SYSTEMS;

EID: 77952269811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1739041.1739082     Document Type: Conference Paper

References (26)

1. L. Antova, T. Jansen, C. Koch, and D. Olteanu. Fast and simple relational processing of uncertain data. In ICDE, pages 983-992, 2008.
2. O. Benjelloun, A. D. Sarma, A. Y. Halevy, and J. Widom. Uldbs: Databases with uncertainty and lineage. In VLDB, pages 953-964, 2006.
3. H. L. Bodlaender. A linear time algorithm for finding tree-decompositions of small treewidth. In STOC '93: Proceedings of the twenty-fifth annual ACM symposium on Theory of computing, pages 226-234, New York, NY, USA, 1993. ACM.
4. 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 Conference, pages 891-893, 2005.
5. R. G. Cowell, S. L. Lauritzen, A. P. David, and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1999.
6. P. Dagum and M. Luby. An optimal approximation algorithm for bayesian inference. Artif. Intell., 93(1-2):1-27, 1997.
7. N. Dalvi and D. Suciu. Management of probabilistic data: foundations and challenges. In PODS, pages 1-12, New York, NY, USA, 2007. ACM Press.
8. N. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, pages 864-875, 2004.
9. N. N. Dalvi and D. Suciu. The dichotomy of conjunctive queries on probabilistic structures. In PODS, pages 293-302, 2007.
10. E. Fischer, J. A. Makowsky, and E. V. Ravve. Counting truth assignments of formulas of bounded tree-width or clique-width. Discrete Applied Mathematics, 156(4):511-529, 2008.
11. J. Huang, L. Antova, C. Koch, and D. Olteanu. "MayBMS: A Probabilistic Database Management System". In Proc. SIGMOD, 2009.
12. J. Huang and A. Darwiche. Using dpll for efficient obdd construction. In SAT, 2004.
13. 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 Conference, pages 687-700, 2008.
14. A. Jha, D. Olteanu, and D. Suciu. Bridging the gap between intensional and extensional query evaluation in probabilistic databases. Technical Report, UW-CSE-10-01-01, 2010.
15. M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2):183-233, 1999.
16. C. Koch and D. Olteanu. Conditioning probabilistic databases. PVLDB, 1(1):313-325, 2008.
17. D. Olteanu and J. Huang. Using obdds for efficient query evaluation on probabilistic databases. In SUM, pages 326-340, 2008.
18. D. Olteanu, J. Huang, and C. Koch. Sprout: Lazy vs. eager query plans for tuple-independent probabilistic databases. In ICDE, pages 640-651, 2009.
19. D. Olteanu, J. Huang, and C. Koch. Approximate confidence computation on probabilistic databases. In ICDE, 2010.
20. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1988.
21. C. Re, N. N. Dalvi, and D. Suciu. Efficient top-k query evaluation on probabilistic data. In ICDE, pages 886-895, 2007.
22. I. Rish. Efficient reasoning in graphical models. PhD thesis, UCI, 1999. Chair-Dechter, Rina.
23. A. D. Sarma, M. Theobald, and J. Widom. Exploiting lineage for confidence computation in uncertain and probabilistic databases. In ICDE, pages 1023-1032, 2008.
24. M. Sauerhoff, I. Wegener, and R. Werchner. Optimal ordered binary decision diagrams for read-once formulas. Discrete Applied Mathematics, 103(1-3):237-258, 2000.
25. P. Sen and A. Deshpande. Representing and querying correlated tuples in probabilistic databases. In In ICDE, 2007.
26. J. S. Yedidia, W. T. Freeman, and Y. Weiss. Generalized belief propagation. In NIPS, pages 689-695, 2000.