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.
CITATION STYLE
Rudra, A., Gopalan, R. P., & Achuthan, N. R. (2014). Estimating sufficient sample sizes for approximate decision support queries. In Lecture Notes in Business Information Processing (Vol. 190, pp. 85–99). Springer Verlag. https://doi.org/10.1007/978-3-319-09492-2_6
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