Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Theory of linear and integer programming
Theory of linear and integer programming
Statistical analysis with missing data
Statistical analysis with missing data
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Operations Research
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Machine Learning
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
OLAP over uncertain and imprecise data
The VLDB Journal — The International Journal on Very Large Data Bases
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Real-world data sets often contain errors and inconsistency. Even though this is a very important problem it has received relatively little attention in the research community. In this paper we examine the problem of learning missing values when some summary information is available. We use linear algebra and constraint programming techniques to learn the missing values using apriori-known summary information and that derived from the raw data. We reconstruct the missing values by different methods in three scenarios: ideal-constrained, under-constrained, and over-constrained. Furthermore, for a range query involving missing values, we also give the lower bound and upper bound for the values using constraint programming techniques. We believe that theory of linear algebra and constraint programming constitutes a sound basis for learning missing values when summary information is available.