Estimating Sufficient Sample Sizes for Approximate Decision Support Queries

Rudra, Amit; Gopalan, Raj; Achuthan, Narasimaha

doi:10.1007/978-3-319-09492-2_6

Access Status

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Authors

Rudra, Amit

Gopalan, Raj

Achuthan, Narasimaha

Date

2014

Type

Book Chapter

Metadata

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Citation

Rudra, A. and Gopalan, R. and Achuthan, N. 2014. Estimating Sufficient Sample Sizes for Approximate Decision Support Queries. In S. Hammoudi, J. Cordeiro, L.A. Maciaszek, J. Filipe (eds), Enterprise Information Systems, 85-99. Switzerland: Springer.

Source Title

Enterprise Information Systems

DOI

10.1007/978-3-319-09492-2_6

ISBN

9783319094915

School

School of Information Systems

URI

http://hdl.handle.net/20.500.11937/13163

Collection

Curtin Research Publications

Abstract

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.