Probabilistic databases with marko views

Proceedings of the VLDB Endowment

Volumn 5, Issue 11, 2012, Pages 1160-1171

  Jha, Abhay ^a   Suciu, Dan ^a  

^a UNIVERSITY OF WASHINGTON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; COMPLEX NETWORKS; DATABASE SYSTEMS; TREES (MATHEMATICS);

COMPLEX CORRELATION; CONJUNCTIVE QUERIES; EFFICIENT QUERY EVALUATION; INDEPENDENT RANDOM EVENTS; MARKOV LOGIC NETWORKS; PROBABILISTIC DATABASE; PROBABILISTIC RELATIONS; TREE DECOMPOSITION;

QUERY PROCESSING;

EID: 84873170216     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/2350229.2350236     Document Type: Article

References (37)

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. M. S. Bartlett. Negative probability. Mathematical Proceedings of the Cambridge Philosophical Society, 41(01):71-73, 1945.
3. E. Birnbaum and E. L. Lozinskii. The good old davis-putnam procedure helps counting models. J. Artif. Intell. Res. (JAIR), 10(1):457-477, 1999.
4. J. Boulos, N. Dalvi, B. Mandhani, S. Mathur, C. Re, and D. Suciu. Mystiq: a system for finding more answers by using probabilities. In SIGMOD, pages 891-893, 2005.
5. R. E. Bryant. Symbolic manipulation of boolean functions using a graphical representation. In DAC, pages 688-694, 1985.
6. M. Burgin. Interpretations of negative probabilities. CoRR, arXiv:1008.1287v1, 2010.
7. N. Dalvi and D. Suciu. Efficient query evaluation on probabilistic databases. VLDB Journal, 16(4):523-544, 2007.
8. N. N. Dalvi, K. Schnaitter, and D. Suciu. Computing query probability with incidence algebras. In PODS, pages 203-214, 2010.
9. P. Domingos and D. Lowd. Markov Logic: An Interface Layer for Artificial Intelligence. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2009.
10. O. Dubois. Counting the number of solutions for instances of satisfiability. Theor. Comput. Sci., 81(1):49-64, 1991.
11. W. Gatterbauer, A. K. Jha, and D. Suciu. Dissociation and propagation for efficient query evaluation over probabilistic databases. In MUD, pages 83-97, 2010.
12. V. Gogate and P. Domingos. Probabilistic theorem proving. In UAI, pages 256-265, 2011.
13. E. G. Haug. Why so negative to negative probabilities? Wilmott Magazine, http://www.wilmott.com/article.cfm?PageNum_rsQueryArticlesMain=3&NoCookies=Yes&forumid=1, 2011.
14. J. Huang and A. Darwiche. Using dpll for efficient obdd construction. In Theory and Applications of Satisfiability Testing, volume 3542 of Lecture Notes in Computer Science, pages 899-899. 2005.
15. A. K. Jha and D. Suciu. Knowledge compilation meets database theory: compiling queries to decision diagrams. In ICDT, pages 162-173, 2011.
16. B. Kanagal and A. Deshpande. Lineage processing over correlated probabilistic databases. In SIGMOD, pages 675-686, 2010.
17. C. Koch and D. Olteanu. Conditioning probabilistic databases. PVLDB, 1(1):313-325, 2008.
18. S. Kok and P. Domingos. Learning the structure of markov logic networks. In ICML, pages 441-448, 2005.
19. M. Ley. The dblp computer science bibliography. http://www.informatik.uni-trier.de/~ley/db/.
20. F. Niu, C. Ré, A. Doan, and J. W. Shavlik. Tuffy: Scaling up statistical inference in markov logic networks using an rdbms. PVLDB, 4(6):373-384, 2011.
21. D. Olteanu and J. Huang. Using OBDDs for efficient query evaluation on probabilistic databases. In SUM, pages 326-340, 2008.
22. D. Olteanu and J. Huang. Secondary-storage confidence computation for conjunctive queries with inequalities. In SIGMOD, pages 389-402, 2009.
23. D. Olteanu, J. Huang, and C. Koch. Sprout: Lazy vs. eager query plans for tuple-independent probabilistic databases. In ICDE, pages 640-651, 2009.
24. D. Olteanu, J. Huang, and C. Koch. Approximate confidence computation in probabilistic databases. In ICDE, pages 145 - 156, 2010.
25. H. Poon and P. Domingos. Sound and efficient inference with probabilistic and deterministic dependencies. In AAAI, pages 458-463, 2006.
26. H. Poon and P. Domingos. Joint inference in information extraction. In AAAI, pages 913-918, 2007.
27. H. Poon and P. Domingos. Unsupervised ontology induction from text. In ACL, pages 296-305, 2010.
28. C. Re, N. N. Dalvi, and D. Suciu. Efficient top-k query evaluation on probabilistic data. In ICDE, pages 886-895, 2007.
29. M. Richardson and P. Domingos. Markov logic networks. Machine Learning, 62(1-2):107-136, 2006.
30. P. Singla and P. Domingos. Discriminative training of markov logic networks. In AAAI, pages 868-873, 2005.
31. P. Singla and P. Domingos. Entity resolution with markov logic. In ICDM, pages 572-582, 2006.
32. F. Somenzi. CUDD: CU Decision Diagram Package Release 2.4.2. http://vlsi.colorado.edu/~fabio/CUDD/, 2011.
33. D. Suciu, D. Olteanu, C. Ré, and C. Koch. Probabilistic Databases. Synthesis Lectures on Data Management. Morgan & Claypool Publishers, 2011.
34. D. Z. Wang, M. J. Franklin, M. N. Garofalakis, J. M. Hellerstein, and M. L. Wick. Hybrid in-database inference for declarative information extraction. In SIGMOD, pages 517-528, 2011.
35. I. Wegener. BDDs-design, analysis, complexity, and applications. Discrete Applied Mathematics, 138(1-2):229-251, 2004.
36. M. L. Wick, A. McCallum, and G. Miklau. Scalable probabilistic databases with factor graphs and mcmc. PVLDB, 3(1):794-804, 2010.
37. Alchemy - Open Source AI. http://alchemy.cs.washington.edu/.