Representing and querying correlated tuples in probabilistic databases

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2007, Pages 596-605

  Sen, Prithviraj ^a   Deshpande, Amol ^a  

^a University of Maryland

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER GRAPHICS; OPTIMIZATION; PROBABILISTIC LOGICS; QUERY PROCESSING; SEMANTICS;

INFERENCE PROBLEM; PROBABILISTIC DATABASES; PROBABILISTIC TUPLES; QUERY EVALUATION; REAL WORLD APPLICATIONS;

DATABASE SYSTEMS;

EID: 34548772330     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2007.367905     Document Type: Conference Paper

References (38)

1. P. Andritsos, A. Fuxman, and R. J. Miller. Clean answers over dirty databases. In ICDE, 2006.
2. D. Barbara, H. Garcia-Molina, and D. Porter. The management of probabilistic data. In KDE, 1992.
3. O. Benjelloun, A. Das Sarma, A. Halevy, and J. Widom. Uldbs: Databases with uncertainty and lineage. In VLDB, 2006.
4. P. Bosc and O. Pivert. About projection-selection-join queries addressed to possibilistic relational, databases. In IEEE Tran. on Fuzzy Systems, 2005.
5. B. P. Buckles and F. E. Petty. A fuzzy model, for relational databases. Intl. Journal of Fuzzy Sets and Systems, 1982.
6. R. Cavallo and M. Pittarelli. The theory of probabilistic databases. In VLDB, 1987.
7. R. Cheng, D. Kalashnikov, and S. Prabhakar. Evaluating probabilistic queries over imprecise data. In SIGMOD, 2003.
8. S. Choenni, H. E. Blok, and E. Leertouwer. Handling uncertainty and ignorance in databases: A rule to combine dependent data. In Database Systems for Adv. Apps., 2006.
9. G. F. Cooper. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, 1990.
10. R. Cowell, S. Lauritzen, and D. Spiegelhater. Probabilistic Networks and Expert Systems. Springer, New York, 1999.
11. P. Dagum and M. Luby. Approximate probabilistic reasoning in Bayesian belief networks is NP-hard. Artificial Intelligence, 1993.
12. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. In VLDB, 2004.
13. N. Dalvi and D. Suciu. Query answering using statistics and probabilistic views. In VLDB, 2005.
14. A. Das S arma, O. Benjelloun, A. Halevy, and J. Widom. Working models for uncertain data. In ICDE, 2006.
15. R. Dechter. Bucket elimination: A unifying framework for probabilistic inference. In UAI, 1996.
16. A. Deshpande, C. Guestrin, S. Madden, J. M. Hellerstein, and W Hong. Model-driven data acquisition in sensor networks. In VLDB, 2004.
17. D. Dey and S. Sarkar. A probabilistic relational model and algebra. ACM Trans. on Database Systems, 1996.
18. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In IJCAI, 1999.
19. N. Fuhr and T. Rolleke. A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Trans. on Information Systems, 1997.
20. M. Garofalakis and D. Suciu, editors. IEEE Data Engineering Bulletin Special Issue on Probabilistic Data Management. March 2006.
21. L. Getoor, B. Taskar, and D. Koller. Selectivity estimation using probabilistic models. In SIGMOD, 2001.
22. C. L. Giles, K. Bollacker, and S. Lawrence. Citeseer: An automatic citation indexing system. In Conf. on Digital Libraries, 1998.
23. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter. Markov Chain Monte Carlo in Practice. Chapman & Hall, 1996.
24. G. Grahne. Horn tables - an efficient tool for handling incomplete information in databases. In PODS, 1989.
25. J. Halpern. An analysis of first-order logics for reasoning about probability. Artificial Intelligence, 1990.
26. http://db.apache.org/derby. The Apache Derby Project.
27. T. Imielinski and W. Lipski, Jr. Incomplete information in relational databases. Journal of the ACM, 1984.
28. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 1999.
29. L. V. S. Lakshmanan, N. Leone, R. Ross, and V. S. Subrahmanian. Probview: a flexible probabilistic database system. ACM Trans. on Database Systems, 1997.
30. S. K. Lee. An extended relational database model for uncertain and imprecise information. In VLDB, 1992.
31. J. Pearl. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, 1988.
32. H. Prade and C. Testemale. Generalizing database relational algebra for the treatment of incomplete or uncertain information and vague queries. Information Sciences, 1984.
33. I. Rish. Efficient Reasoning in Graphical Models. PhD thesis, University of California, Irvine, 1999.
34. P. Sen and A. Deshpande. Representing and querying correlated tuples in probabilistic databases. Technical Report CS-TR-4820, University of Maryland, College Park, 2006.
35. E. Ukkonen. Approximate string matching with q-grams and maximal matches. In Theoretical Computer Science, 1992.
36. J. S. Vitter. External memory algorithms and data structures: Dealing with massive data. ACM Computing Surveys, 2001.
37. N. L. Zhang and D. Poole. A simple approach to Bayesian network computations. In Canadian Artif. Intel. Conf., 1994.
38. N. L. Zhang and D. Poole. Exploiting causal independence in Bayesian network inference. Journal of Artificial Intelligence Research, 1996.