Skip to main content
SpringerLink
Log in

Probabilistic Interval XML

  • Conference paper
  • First Online:
Database Theory — ICDT 2003 (ICDT 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2572))

Included in the following conference series:

Abstract

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.

University of Maryland Institute for Advanced Computer Studies

Institute for Advanced Computer Studies and Institute for Systems Research

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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.

    Article  Google Scholar 

  2. G. Boole. (1854) The Laws of Thought, Macmillan, London.

    Google Scholar 

  3. B. Bouwerman and R.T. O’Connell. (2000) Forecasting and Time Series: An Applied Approach, Brooks/Cole Publishing.

    Google Scholar 

  4. R. Cavallo and M. Pittarelli. (1987) The Theory of Probabilistic Databases, in Proc. VLDB’87.

    Google Scholar 

  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. D. Dey and S. Sarkar. (1996) AProbabilistic Relational Model andAlgebra,ACMTransactions on Database Systems, Vol. 21, 3, pp 339–369.

    Article  Google Scholar 

  7. C. Dyreson and R. Snodgrass. (1998) Supporting Valid-Time Indeterminacy, ACM Transactions on Database Systems, Vol. 23, Nr. 1, pp 1–57.

    Article  Google Scholar 

  8. Fagin, R., J. Y. Halpern, and N. Megiddo (1990). A logic for reasoning about probabilities. Information and Computation 87(1/2), 78–128.

    Article  MathSciNet  MATH  Google Scholar 

  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. 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. G. Kamberova and R. Bajcsy. (1998) Stereo Depth Estimation: the Confidence Interval Approach, Proc. Intl. Conf. Computer Vision ICCV98, Bombay, India, Jan. 1998

    Google Scholar 

  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. 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.

    Article  Google Scholar 

  14. V.S. Lakshmanan and F. Sadri. (1994) Probabilistic Deductive Databases, in Proc. Int. Logic Programming Symp., (ILPS’94), MIT Press.

    Google Scholar 

  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. 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. 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. J. Pearl. (1988) Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann.

    Google Scholar 

  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. S. Ross. (1998) A First Course in Probability, Prentice Hall, 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Maryland, College Park, 20742, MD

    Edward Hung, Lise Getoor & V. S. Subrahmanian

Authors
  1. Edward Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lise Getoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. V. S. Subrahmanian
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dip. di Informatica e Sistemistica, Università di Roma “La Sapienza”, Via Salaria 113, 00198, Roma, Italy

    Diego Calvanese  & Maurizio Lenzerini  & 

  2. Department of Computer Science, Stanford University, Room 474 Gates Computer Science Building 4B, 94305-9045, Stanford, CA, USA

    Rajeev Motwani

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hung, E., Getoor, L., Subrahmanian, V.S. (2003). Probabilistic Interval XML. In: Calvanese, D., Lenzerini, M., Motwani, R. (eds) Database Theory — ICDT 2003. ICDT 2003. Lecture Notes in Computer Science, vol 2572. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36285-1_24

Download citation

  • DOI: https://doi.org/10.1007/3-540-36285-1_24

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00323-6

  • Online ISBN: 978-3-540-36285-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation