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.
CITATION STYLE
Sadri, F., & Tallur, G. (2016). Integration of probabilistic information. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9877 LNCS, pp. 179–190). Springer Verlag. https://doi.org/10.1007/978-3-319-46922-5_14
Mendeley helps you to discover research relevant for your work.