Skip to main content

Uncertainty That Counts

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7022))

Abstract

Uncertainty is modeled by a multibase (db,μ) where db is a database with zero or more primary key violations, and μ associates a multiplicity (a positive integer) to each fact of db. In data integration, the multiplicity of a fact g can indicate the number of data sources in which g was found. In planning databases, facts with the same primary key value are alternatives for each other, and the multiplicity of a fact g can denote the number of people in favor of g.

A repair of db is obtained by selecting a maximal number of facts without ever selecting two distinct facts of the same relation that agree on their primary key. Every repair has a support count, which is the product of the multiplicities of its facts.

For a fixed Boolean query q, we define σ CERTAINTY(q) as the following counting problem: Given a multibase (db,μ), determine the weighted number of repairs of db that satisfy q. Here, every repair is weighted by its support count. We illustrate the practical significance of this problem by means of examples.

For conjunctive queries q without self-join, we provide a syntactic characterization of the class of queries q such that σ CERTAINTY(q) is in P; for queries not in this class, σ CERTAINTY(q) is \(\sharp\) P-hard (and hence highly intractable).

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

Buying options

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Reading (1995)

    MATH  Google Scholar 

  2. Arenas, M., Bertossi, L.E., Chomicki, J.: Consistent query answers in inconsistent databases. In: PODS, pp. 68–79. ACM Press, New York (1999)

    Google Scholar 

  3. Arenas, M., Bertossi, L.E., Chomicki, J., He, X., Raghavan, V., Spinrad, J.: Scalar aggregation in inconsistent databases. Theor. Comput. Sci. 296(3), 405–434 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dalvi, N.N., Ré, C., Suciu, D.: Probabilistic databases: diamonds in the dirt. Commun. ACM 52(7), 86–94 (2009)

    Article  Google Scholar 

  5. Dalvi, N.N., Re, C., Suciu, D.: Queries and materialized views on probabilistic databases. J. Comput. Syst. Sci. 77(3), 473–490 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Dalvi, N.N., Suciu, D.: Management of probabilistic data: foundations and challenges. In: Libkin, L. (ed.) PODS, pp. 1–12. ACM, New York (2007)

    Google Scholar 

  7. Fan, W., Geerts, F., Wijsen, J.: Determining the currency of data. In: Lenzerini, M., Schwentick, T. (eds.) PODS, pp. 71–82. ACM, New York (2011)

    Google Scholar 

  8. Fuxman, A., Miller, R.J.: First-order query rewriting for inconsistent databases. J. Comput. Syst. Sci. 73(4), 610–635 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  9. Greco, S., Molinaro, C.: Approximate probabilistic query answering over inconsistent databases. In: Li, Q., Spaccapietra, S., Yu, E.S.K., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 311–325. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  10. Maslowski, D., Wijsen, J.: On counting database repairs. In: Proceedings of the 4th International Workshop on Logic in Databases, LID 2011, pp. 15–22. ACM, New York (2011), http://doi.acm.org/10.1145/1966357.1966361

    Google Scholar 

  11. Pema, E., Kolaitis, P.G., Tan, W.C.: On the tractability and intractability of consistent conjunctive query answering. In: Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop, PhD 2011, pp. 38–44. ACM, New York (2011), http://doi.acm.org/10.1145/1966874.1966881

    Google Scholar 

  12. Toda, S.: PP is as hard as the polynomial-time hierarchy. SIAM J. Comput. 20(5), 865–877 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  13. Wijsen, J.: On the consistent rewriting of conjunctive queries under primary key constraints. Inf. Syst. 34(7), 578–601 (2009)

    Article  Google Scholar 

  14. Wijsen, J.: On the first-order expressibility of computing certain answers to conjunctive queries over uncertain databases. In: Paredaens, J., Gucht, D.V. (eds.) PODS, pp. 179–190. ACM, New York (2010)

    Google Scholar 

  15. Wijsen, J.: A remark on the complexity of consistent conjunctive query answering under primary key violations. Inf. Process. Lett. 110(21), 950–955 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Maslowski, D., Wijsen, J. (2011). Uncertainty That Counts. In: Christiansen, H., De Tré, G., Yazici, A., Zadrozny, S., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2011. Lecture Notes in Computer Science(), vol 7022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24764-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24764-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24763-7

  • Online ISBN: 978-3-642-24764-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics