Deriving probabilistic databases with inference ensembles

Julia Stoyanovich, Susan Davidson, Tova Milo, Val Tannen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Many real-world applications deal with uncertain or missing data, prompting a surge of activity in the area of probabilistic databases. A shortcoming of prior work is the assumption that an appropriate probabilistic model, along with the necessary probability distributions, is given. We address this shortcoming by presenting a framework for learning a set of inference ensembles, termed meta-rule semi-lattices, or MRSL, from the complete portion of the data. We use the MRSL to infer probability distributions for missing data, and demonstrate experimentally that high accuracy is achieved when a single attribute value is missing per tuple. We next propose an inference algorithm based on Gibbs sampling that accurately predicts the probability distribution for multiple missing values. We also develop an optimization that greatly improves performance of multi-attribute inference for collections of tuples, while maintaining high accuracy. Finally, we develop an experimental framework to evaluate the efficiency and accuracy of our approach.

    Original languageEnglish (US)
    Title of host publication2011 IEEE 27th International Conference on Data Engineering, ICDE 2011
    Number of pages12
    StatePublished - 2011
    Event2011 IEEE 27th International Conference on Data Engineering, ICDE 2011 - Hannover, Germany
    Duration: Apr 11 2011Apr 16 2011

    Publication series

    NameProceedings - International Conference on Data Engineering
    ISSN (Print)1084-4627


    Other2011 IEEE 27th International Conference on Data Engineering, ICDE 2011

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Information Systems


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