Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Exploratory Data Mining and Data Cleaning
Exploratory Data Mining and Data Cleaning
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Clean Answers over Dirty Databases: A Probabilistic Approach
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
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VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment
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Proceedings of the VLDB Endowment
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Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Probabilistic graph summarization
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Real-world databases often contain syntactic and semantic errors, in spite of integrity constraints and other safety measures incorporated into modern DBMSs. We present ERACER, an iterative statistical framework for inferring missing information and correcting such errors automatically. Our approach is based on belief propagation and relational dependency networks, and includes an efficient approximate inference algorithm that is easily implemented in standard DBMSs using SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated manner, using shrinkage techniques to infer correct values accurately even in the presence of dirty data. We evaluate the proposed methods empirically on multiple synthetic and real-world data sets. The results show that our framework achieves accuracy comparable to a baseline statistical method using Bayesian networks with exact inference. However, our framework has wider applicability than the Bayesian network baseline, due to its ability to reason with complex, cyclic relational dependencies.