Optimizing mpf queries: Decision support and probabilistic inference

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2007, Pages 701-712

  Bravo, Héctor Corrada ^a   Ramakrishnan, Raghu ^a,b  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b YAHOO RESEARCH   (United States)

Author keywords

Aggregate queries; Probabilistic inference

Indexed keywords

DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; INFORMATION RETRIEVAL; LEARNING SYSTEMS; PROBABILITY DISTRIBUTIONS; QUERY PROCESSING;

AGGREGATE QUERIES; BELIEF PROPAGATION; PROBABILISTIC INFERENCE; QUERY OPTIMIZATION;

INFORMATION MANAGEMENT;

EID: 35449003720     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1247480.1247558     Document Type: Conference Paper

References (27)

1. S. Aji and R. McEliece. The generalized distributive law. IEEE Trans. Info. Theory, 46(2):325-343, March 2000.
2. W. L. Buntine. Operations for learning with graphical models. J. Artif. Intell. Res. (JAIR), 2:159-225, 1994.
3. D. Burdick, P. Deshpande, T. S. Jayram, R. Ramakrishnan, and S. Vaithyanathan. Olap over uncertain and imprecise data. In VLDB, pages 970-981, 2005.
4. S. Chaudhuri and K. Shim. Including Group-By in Query Optimization. In VLDB, pages 354-366, 1994.
5. S. Chaudhuri and K. Shim. Optimizing queries with aggregate views. In Proc. 5th Int'nl. Conf. on Extending DB Technology, pages 167-182. Springer-Verlag, 1996.
6. R. G. Cowell, A. P. Dawid, S. L. Lauritzen, and D. J. Spiegelhalter. Probabilistic Networks and Expert Systems. Springer-Verlag, New York, 1999.
7. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, 2004.
8. N. N. Dalvi and D. Suciu. Answering queries from statistics and probabilistic views. In VLDB, pages 805-816, 2005.
9. Y. E. Fattah and R. Dechter. An evaluation of structural parameters for probabilistic reasoning: Results on benchmark circuits. In UAI, pages 244-251, 1996.
10. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI, pages 1300-1309, 1999.
11. N. Fuhr and T. RòTleke. A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Trans. Inf. Syst., 15(11):32-66, 1997.
12. D. Heckerman. A tutorial on learning with bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, 1999.
13. D. Heckerman, C. Meek, and D. Koller. Probabilistic entity-relationship models, prms and plate models. In SRL2004. ICML, August 2004.
14. F. V. Jensen. Bayesian networks and decision graphs. Springer-Verlag, 2001.
15. F. R. Kschischang, B. J. Frey, and H.-A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Trans. Info. Theory, 47(2):498-519, 2001.
16. J. Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
17. C. Ré, N. Dalvi, and D. Suciu. Efficient top-k query evaluation on probabilistic data. Technical Report 2006-06-05, University of Washington, 2006.
18. C. Re, N. Dalvi, and D. Suciu. Query evaluation on probabilistic databases. IEEE Data Engineering Bulletin, 29(1):25-31, 2006.
19. P. G. Selinger, M. M. Astrahan, D. D. Chamberlin, R. A. Lorie, and T. G. Price. Access path selection in a relational database management system. In SIGMOD, pages 23-34, 1979.
20. P. Singla and P. Domingos. Discriminative training of markov logic networks. In AAAI, pages 868-873, 2005.
21. M. Wainwright and M. Jordan. Graphical models, exponential families and variational inference. Technical Report 649, Department of Statistics, University of California, Berkeley, 2003.
22. Y. Weiss. Correctness of local probability propagation in graphical models with loops. Neural Computation, 12:1-41, 2000.
23. S. K. M. Wong. The relational structure of belief networks. J. Intell. Inf. Syst., 16(2):117-148, 2001.
24. S. K. M. Wong, C. J. Butz, and Y. Xiang. A method for implementing a probabilistic model as a relational database. In UAI, pages 556-564, 1995.
25. S. K. M. Wong, D. Wu, and C. J. Butz. Probabilistic reasoning in bayesian networks: A relational database approach. In Canadian Conference on AI, pages 583-590, 2003.
26. J. S. Yedidia, W. T. Freeman, and Y. Weiss. Constructing free energy approximations and generalized belief propagation algorithms. Technical Report TR.-2002-35, Mitsubishi Electric Research Laboratories, 2002.
27. N. L. Zhang and D. Poole. Exploiting causal independence in bayesian network inference. JAIR, 5:301-328, 1996.