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Graphical Models for Uncertain Data Management

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  • First Online:
Encyclopedia of Database Systems

  • 54 Accesses

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...

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References

  1. Aggarwal CC. Managing and mining uncertain data. New York: Springer Incorporated; 2009.

    Google Scholar 

  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 (SIGMOD); 2003.

    Google Scholar 

  3. Cowell RG, Philip Dawid A, Lauritzen SL, Spiegelhater DJ. Probabilistic networks and expert systems. New York: Springer; 1999.

    Google Scholar 

  4. Dalvi N, Suciu D. Efficient query evaluation on probabilistic databases. Int J Very Large Data Bases (VLDB). 2006.

    Google Scholar 

  5. Das Sarma A, Benjelloun O, Halevy A, Widom J. Working models for uncertain data. In: International Conference on Data Engineering; 2006.

    Google Scholar 

  6. Deshpande A, Guestrin C, Madden S, Hellerstein JM, Hong W. Model-driven data acquisition in sensor networks. In: International Conference on Very Large Data Bases; 2004.

    Google Scholar 

  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. 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.

    Google Scholar 

  9. Getoor L, Taskar B, editors. Introduction to statistical relational learning. Cambridge: MIT Press; 2007.

    Google Scholar 

  10. Jayram TS, Krishnamurthy R, Raghavan S, Vaithyanathan S, Zhu H. Avatar information extraction system. In: IEEE Data Engineering Bulletin; 2006.

    Google Scholar 

  11. Jha A, Suciu D. Probabilistic databases with markoviews. Proc VLDB Endowment (PVLDB). 2012;5(11):1160–71.

    Google Scholar 

  12. Jordan MI, editor. Learning in graphical models. Cambridge: MIT Press; 1999.

    Google Scholar 

  13. Jordan MI, Ghahramani Z, Jaakkola TS, Saul LK. An introduction to variational methods for graphical models. Mach Learn. 1999;37(2):183–233.

    Google Scholar 

  14. Kanagal B, Deshpande A. Online filtering, smoothing and probabilistic modeling of streaming data. In: ICDE; 2008.

    Google Scholar 

  15. Kanagal B, Deshpande A. Indexing correlated probabilistic databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD); 2009. p. 455–68.

    Google Scholar 

  16. Kanagal B, Deshpande A. Lineage processing on correlated probabilistic databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD); 2010.

    Google Scholar 

  17. Murphy KP, Weiss Y, Jordan MI. Loopy belief propagation for approximate inference: an empirical study. In: UAI; 1999. p. 467–75.

    Google Scholar 

  18. Pearl J. Probabilistic reasoning in intelligent systems. San Mateo: Morgan Kaufmann; 1988.

    Google Scholar 

  19. Poole D. First-order probabilistic inference. In: International Joint Conferences on Artificial Intelligence; 2003.

    Google Scholar 

  20. Rekatsinas T, Deshpande A, Getoor L. Local structure and determinism in probabilistic databases. In: SIGMOD Conference; 2012. p. 373–84.

    Google Scholar 

  21. Sen P, Deshpande A, Getoor L. Exploiting shared correlations in probabilistic databases. In: VLDB; 2008.

    Google Scholar 

  22. Sen P, Deshpande A, Getoor L. PrDB: managing and exploiting rich correlations in probabilistic databases. VLDB J. 2009;18(5):1065–90.

    Article  Google Scholar 

  23. Suciu D, Olteanu D, Ré C, Koch C. Probabilistic databases. Synth Lect Data Manag. 2011;3(2):1–180.

    Article  MATH  Google Scholar 

  24. Wick M, McCallum A, Miklau G. Scalable probabilistic databases with factor graphs and MCMC. Proc VLDB Endowment. 2010;3(1–2):794–804.

    Article  Google Scholar 

  25. Zhe Wang D, Michelakis E, Garofalakis M, Hellerstein JM. Bayesstore: managing large, uncertain data repositories with probabilistic graphical models. Proc VLDB Endowment (PVLDB). 2008;1(1):340–51.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Maryland, College Park, MD, USA

    Amol Deshpande

Authors
  1. Amol Deshpande
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Correspondence to Amol Deshpande .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. Technical University of Crete, Chania, Greece

    Minos Garofalakis

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Deshpande, A. (2017). Graphical Models for Uncertain Data Management. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_80741-1

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_80741-1

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4899-7993-3

  • Online ISBN: 978-1-4899-7993-3

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