Estimating Sufficient Sample Sizes for Approximate Decision Support Queries
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Sampling schemes for approximate processing of highly selective decision support queries need to retrieve sufficient number of records that can provide reliable results within acceptable error limits. The k-MDI tree is an innovative index structure that supports drawing rich samples of relevant records for a given set of dimensional attribute ranges. This paper describes a method for estimating sufficient sample sizes for decision support queries based on inverse simple random sampling without replacement (SRSWOR). Combined with a k-MDI tree index, this method is shown to offer a reliable approach to approximate query processing for decision support.
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Rudra, Amit; Gopalan, Raj; Achuthan, Narasimaha (2012)Decision support queries usually involve accessing enormous amount of data requiring significant retrieval time. Faster retrieval of query results can often save precious time for the decision maker. Pre-computation of ...
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