Véronique Stéphan

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Dealing with large volumes of data, OLAP data cubes aggregated values are often spoiled by errors due to missing values in detailed data. This paper suggests to adjust aggregate answers, noticing that non-missing values constitute a biased sample of the true result of the query. Using basic random sampling theory, we show that two different problems can be(More)
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