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.
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References
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)
Agrawal, P., Sarma, A.D., Ullman, J.D., Widom, J.: Foundations of uncertain-data integration. Proc. VLDB Endowment 3(1), 1080–1090 (2010)
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)
Barbará, D., Garcia-Molina, H., Porter, D.: The management of probabilistic data. IEEE Trans. Knowl. Data Eng. 4(5), 487–502 (1992)
Benjelloun, O., Sarma, A.D., Halevy, A.Y., Theobald, M., Widom, J.: Databases with uncertainty and lineage. VLDB J. 17(2), 243–264 (2008)
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)
Codd, E.F.: Extending the database relational model to capture more meaning. ACM Trans. Database Syst. 4(4), 397–434 (1979)
Dalvi, N.N., Ré, C., Suciu, D.: Probabilistic databases: diamonds in the dirt. Commun. ACM 52(7), 86–94 (2009)
Dalvi, N.N., Suciu, D.: Efficient query evaluation on probabilistic databases. In: Proceedings of International Conference on Very Large Databases, pp. 864–875 (2004)
Dalvi, N.N., Suciu, D.: Efficient query evaluation on probabilistic databases. VLDB J. 16(4), 523–544 (2007)
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
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)
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)
Sadri, F.: On the foundations of probabilistic information integration. In: Proceedings of International Conference on Information and Knowledge Management, pp. 882–891 (2012)
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
Sadri, F., Tallur, G.: Integration of probabilistic uncertain information (2016). CoRR, abs/1607.05702
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Sadri, F., Tallur, G. (2016). Integration of Probabilistic Information. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_14
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DOI: https://doi.org/10.1007/978-3-319-46922-5_14
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