Integration of Probabilistic Information

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9877)

Abstract

We study the problem of data integration from sources that contain probabilistic uncertain information. Data is modeled by possible-worlds with probability distribution, compactly represented in the probabilistic relation model. Integration is achieved efficiently using the extended probabilistic relation model. We study the problem of determining the probability distribution of the integration result. It has been shown that, in general, only probability ranges can be determined for the result of integration. We show that under intuitive and reasonable assumptions we can determine the exact probability distribution of the result of integration. Our methodologies are presented in possible-worlds as well as probabilistic-relation frameworks.

Keywords

Data integration Probabilistic data Uncertain data Probabilistic relation model 

References

  1. 1.
    Abiteboul, S., Kanellakis, P.C., Grahne, G.: On the representation and querying of sets of possible worlds. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 34–48 (1987)Google Scholar
  2. 2.
    Agrawal, P., Sarma, A.D., Ullman, J.D., Widom, J.: Foundations of uncertain-data integration. Proc. VLDB Endowment 3(1), 1080–1090 (2010)CrossRefGoogle Scholar
  3. 3.
    Antova, L., Jansen, T., Koch, C., Olteanu, D.: Fast and simple relational processing of uncertain data. In: Proceedings of IEEE International Conference on Data Engineering, pp. 983–992 (2008)Google Scholar
  4. 4.
    Barbará, D., Garcia-Molina, H., Porter, D.: The management of probabilistic data. IEEE Trans. Knowl. Data Eng. 4(5), 487–502 (1992)CrossRefGoogle Scholar
  5. 5.
    Benjelloun, O., Sarma, A.D., Halevy, A.Y., Theobald, M., Widom, J.: Databases with uncertainty and lineage. VLDB J. 17(2), 243–264 (2008)CrossRefGoogle Scholar
  6. 6.
    Dayyan Borhanian, A., Sadri, F.: A compact representation for efficient uncertain-information integration. In: Proceedings of International Database Engineering and Applications IDEAS, pp. 122–131 (2013)Google Scholar
  7. 7.
    Codd, E.F.: Extending the database relational model to capture more meaning. ACM Trans. Database Syst. 4(4), 397–434 (1979)CrossRefGoogle Scholar
  8. 8.
    Dalvi, N.N., Ré, C., Suciu, D.: Probabilistic databases: diamonds in the dirt. Commun. ACM 52(7), 86–94 (2009)CrossRefGoogle Scholar
  9. 9.
    Dalvi, N.N., Suciu, D.: Efficient query evaluation on probabilistic databases. In: Proceedings of International Conference on Very Large Databases, pp. 864–875 (2004)Google Scholar
  10. 10.
    Dalvi, N.N., Suciu, D.: Efficient query evaluation on probabilistic databases. VLDB J. 16(4), 523–544 (2007)CrossRefGoogle Scholar
  11. 11.
    Haas, L.: Beauty and the beast: the theory and practice of information integration. In: Schwentick, T., Suciu, D. (eds.) ICDT 2007. LNCS, vol. 4353, pp. 28–43. Springer, Heidelberg (2006). doi:10.1007/11965893_3 CrossRefGoogle Scholar
  12. 12.
    Halevy, A.Y., Rajaraman, A., Ordille, J.J.: Data integration: The teenage years. In: Proceedings of International Conference on Very Large Databases, pp. 9–16 (2006)Google Scholar
  13. 13.
    Liu, K.C., Sunderraman, R.: On representing indefinite and maybe information in relational databases. In: Proceedings of IEEE International Conference on Data Engineering, pp. 250–257 (1988)Google Scholar
  14. 14.
    Sadri, F.: On the foundations of probabilistic information integration. In: Proceedings of International Conference on Information and Knowledge Management, pp. 882–891 (2012)Google Scholar
  15. 15.
    Sadri, F.: Belief revision in uncertain data integration. In: Sharaf, M.A., Cheema, M.A., Qi, J. (eds.) ADC 2015. LNCS, vol. 9093, pp. 78–90. Springer, Heidelberg (2015). doi:10.1007/978-3-319-19548-3_7 CrossRefGoogle Scholar
  16. 16.
    Sadri, F., Tallur, G.: Integration of probabilistic uncertain information (2016). CoRR, abs/1607.05702Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of North CarolinaGreensboroUSA

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