Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Graphical Models for Uncertain Data Management

  • Amol Deshpande
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80741

Synonyms

Bayesian networks; Correlated databases; Markov networks; Probabilistic databases

Definition

Uncertain data appears naturally in many real-world applications for a variety of reasons, ranging from inherent limitations of the measurement or monitoring infrastructures to widespread use of statistical analysis and probabilistic inference. Further, the uncertainties associated with different entities or facts in the data are often correlated with each other. For instance, two facts may be known to be mutually exclusive, i.e., even if we are uncertain about which of the two are true, we may know that both the facts cannot be simultaneously true. Oftentimes the correlations are more complex; for example, given two uncertain facts, we may know that if one of them is true, then the probability for the other being true is higher and vice versa. To manage such correlated data in a principled manner, the uncertain data model must be expressive enough to allow capturing such...

This is a preview of subscription content, log in to check access.

References

  1. 1.
    Aggarwal CC. Managing and mining uncertain data. New York: Springer Incorporated; 2009.zbMATHCrossRefGoogle Scholar
  2. 2.
    Cheng R, Kalashnikov D, Prabhakar S. Evaluating probabilistic queries over imprecise data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2003.Google Scholar
  3. 3.
    Cowell RG, Philip Dawid A, Lauritzen SL, Spiegelhater DJ. Probabilistic networks and expert systems. New York: Springer; 1999.zbMATHGoogle Scholar
  4. 4.
    Dalvi N, Suciu D. Efficient query evaluation on probabilistic databases. In: Proceedings of the 32nd International Conference on Very Large Data Bases; 2006.Google Scholar
  5. 5.
    Das Sarma A, Benjelloun O, Halevy A, Widom J. Working models for uncertain data. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.Google Scholar
  6. 6.
    Deshpande A, Guestrin C, Madden S, Hellerstein JM, Hong W. Model-driven data acquisition in sensor networks. In: Proceedings of the 30th International Conference on Very Large Data Bases; 2004.Google Scholar
  7. 7.
    Deshpande A, Getoor L, Sen P. Graphical models for uncertain data. In: Aggarwal C, editor. Managing and mining uncertain data. New York: Springer; 2009.Google Scholar
  8. 8.
    Fuhr N, Rolleke T. A probabilistic relational algebra for the integration of information retrieval and database systems. ACM Trans Inf Syst. 1997;15(1):32.CrossRefGoogle Scholar
  9. 9.
    Getoor L, Taskar B, editors. Introduction to statistical relational learning. Cambridge: MIT Press; 2007.zbMATHGoogle Scholar
  10. 10.
    Jayram TS, Krishnamurthy R, Raghavan S, Vaithyanathan S, Zhu H. Avatar information extraction system. In: IEEE Data Engineering Bulletin; 2006.Google Scholar
  11. 11.
    Jha A, Suciu D. Probabilistic databases with markoviews. Proc VLDB Endowment. 2012;5(11): 1160–71.CrossRefGoogle Scholar
  12. 12.
    Jordan MI, editor. Learning in graphical models. Cambridge: MIT Press; 1999.Google Scholar
  13. 13.
    Jordan MI, Ghahramani Z, Jaakkola TS, Saul LK. An introduction to variational methods for graphical models. Mach Learn. 1999;37(2):183–233.zbMATHCrossRefGoogle Scholar
  14. 14.
    Kanagal B, Deshpande A. Online filtering, smoothing and probabilistic modeling of streaming data. In: Proceedings of the 24th International Conference on Data Engineering; 2008.Google Scholar
  15. 15.
    Kanagal B, Deshpande A. Indexing correlated probabilistic databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2009. p. 455–68.Google Scholar
  16. 16.
    Kanagal B, Deshpande A. Lineage processing on correlated probabilistic databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2010.Google Scholar
  17. 17.
    Murphy KP, Weiss Y, Jordan MI. Loopy belief propagation for approximate inference: an empirical study. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence; 1999. p. 467–75.Google Scholar
  18. 18.
    Pearl J. Probabilistic reasoning in intelligent systems. San Mateo: Morgan Kaufmann; 1988.zbMATHGoogle Scholar
  19. 19.
    Poole D. First-order probabilistic inference. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence; 2003.Google Scholar
  20. 20.
    Rekatsinas T, Deshpande A, Getoor L. Local structure and determinism in probabilistic databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2012. p. 373–84.Google Scholar
  21. 21.
    Sen P, Deshpande A, Getoor L. Exploiting shared correlations in probabilistic databases. In: Proceedings of the 34th International Conference on Very Large Data Bases; 2008.Google Scholar
  22. 22.
    Sen P, Deshpande A, Getoor L. PrDB: managing and exploiting rich correlations in probabilistic databases. VLDB J. 2009;18(5):1065–90.CrossRefGoogle Scholar
  23. 23.
    Suciu D, Olteanu D, Ré C, Koch C. Probabilistic databases. Synth Lect Data Manag. 2011;3(2): 1–180.zbMATHCrossRefGoogle Scholar
  24. 24.
    Wick M, McCallum A, Miklau G. Scalable probabilistic databases with factor graphs and MCMC. Proc VLDB Endowment. 2010;3(1–2): 794–804.CrossRefGoogle Scholar
  25. 25.
    Zhe Wang D, Michelakis E, Garofalakis M, Hellerstein JM. Bayesstore: managing large, uncertain data repositories with probabilistic graphical models. Proc VLDB Endowment. 2008;1(1):340–51.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of MarylandCollege ParkUSA