Probabilistic Interval XML

  • Edward Hung
  • Lise Getoor
  • V. S. Subrahmanian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2572)


Interest in XML databases has been growing over the last few years. In this paper, we study the problem of incorporating probabilistic information into XML databases. We propose the Probabilistic Interval XML (PIXml for short) data model in this paper. Using this data model, users can express probabilistic information within XML markups. In addition, we provide two alternative formal model-theoretic semantics for PIXml data. The first semantics is a “global” semantics which is relatively intuitive, but is not directly amenable to computation. The second semantics is a “local” semantics which is more amenable to computation. We prove several results linking the two semantics together. To our knowledge, this is the first formal model theoretic semantics for probabilistic interval XML. We then provide an operational semantics that may be used to compute answers to queries and that is correct for a large class of probabilistic instances.


Operational Semantic Deductive Database Path Expression Binary Predicate Probabilistic Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    D. Barbara, H. Garcia-Molina and D. Porter. (1992) The Management of Probabilistic Data, IEEE Trans. on Knowledge and Data Engineering, Vol. 4, pp 487–502.CrossRefGoogle Scholar
  2. 2.
    G. Boole. (1854) The Laws of Thought, Macmillan, London.Google Scholar
  3. 3.
    B. Bouwerman and R.T. O’Connell. (2000) Forecasting and Time Series: An Applied Approach, Brooks/Cole Publishing.Google Scholar
  4. 4.
    R. Cavallo and M. Pittarelli. (1987) The Theory of Probabilistic Databases, in Proc. VLDB’87.Google Scholar
  5. 5.
    A. Dekhtyar, J. Goldsmith and S.R. Hawkes. (2001) Semi-structured Probabilistic Databases, Proceedings of 2001 Conference on Statisitcal and Scientific Database Management (SSDBM), George Mason University, Fairfax, VA, USA, pp. 36–45, July 2001.Google Scholar
  6. 6.
    D. Dey and S. Sarkar. (1996) AProbabilistic Relational Model andAlgebra,ACMTransactions on Database Systems, Vol. 21, 3, pp 339–369.CrossRefGoogle Scholar
  7. 7.
    C. Dyreson and R. Snodgrass. (1998) Supporting Valid-Time Indeterminacy, ACM Transactions on Database Systems, Vol. 23, Nr. 1, pp 1–57.CrossRefGoogle Scholar
  8. 8.
    Fagin, R., J. Y. Halpern, and N. Megiddo (1990). A logic for reasoning about probabilities. Information and Computation 87(1/2), 78–128.CrossRefMathSciNetzbMATHGoogle Scholar
  9. 9.
    U. Guntzer, W. Kiessling and H. Thone. (1991) New Directions for Uncertainty Reasoning in Deductive Databases, Proc. 1991 ACM SIGMOD, pp 178–187.Google Scholar
  10. 10.
    E. Hung, L. Getoor, V.S. Subrahmanian. (2003) PXML: A Probabilistic Semistructured Data Model and Algebra, Proc. of the 19th International Conference on Data Engineering, Bangalore, India, March 5–8, 2003.Google Scholar
  11. 11.
    G. Kamberova and R. Bajcsy. (1998) Stereo Depth Estimation: the Confidence Interval Approach, Proc. Intl. Conf. Computer Vision ICCV98, Bombay, India, Jan. 1998Google Scholar
  12. 12.
    W. Kiessling, H. Thone and U. Guntzer. (1992) Database Support for Problematic Knowledge, Proc. EDBT-92, pp. 421–436, Springer LNCS Vol. 580.Google Scholar
  13. 13.
    V.S. Lakshmanan, N. Leone, R. Ross and V.S. Subrahmanian. (1997) ProbView: A Flexible Probabilistic Database System. ACM Transactions on Database Systems, Vol. 22, Nr. 3, pp. 419–469.CrossRefGoogle Scholar
  14. 14.
    V.S. Lakshmanan and F. Sadri. (1994) Probabilistic Deductive Databases, in Proc. Int. Logic Programming Symp., (ILPS’94), MIT Press.Google Scholar
  15. 15.
    V.S. Lakshmanan and N. Shiri. (1996) Parametric Approach with Deductive Databases with Uncertainty, in Proc. Workshop on Logic In Databases 1996, pp. 61–81.Google Scholar
  16. 16.
    J. McHugh, S. Abiteboul, R. Goldman, D. Quass, and J. Widom. (1997) Lore: A Database Management System for Semistructured Data, ACM SIGMOD Record, Sep. 1997.Google Scholar
  17. 17.
    A. Nierman and H.V. Jagadish. (2002) ProTBD: Probabilistic Data in XML, in Proc. of the 28th International Conference on Very Large Data Bases, Hong Kong, August, 2002.Google Scholar
  18. 18.
    J. Pearl. (1988) Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann.Google Scholar
  19. 19.
    D. Radev, W. Fan and H. Qi. (2002) Probabilistic question answering from the Web, Proc. 11th InternationalWorldWideWeb Conference, Hawaii, May 2002.Google Scholar
  20. 20.
    S. Ross. (1998) A First Course in Probability, Prentice Hall, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Edward Hung
    • 1
  • Lise Getoor
    • 1
  • V. S. Subrahmanian
    • 1
  1. 1.Dept. of Computer ScienceUniversity of Maryland

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