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Estimating Sufficient Sample Sizes for Approximate Decision Support Queries

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Enterprise Information Systems (ICEIS 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 190))

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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.

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Author information

Authors and Affiliations

  1. School of Information Systems, Curtin University, Perth, Australia

    Amit Rudra

  2. Department of Computing, Curtin University, Perth, Australia

    Raj P. Gopalan

  3. Independent Consultant, Perth, Australia

    N. R. Achuthan

Authors
  1. Amit Rudra
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  2. Raj P. Gopalan
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  3. N. R. Achuthan
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Corresponding author

Correspondence to Raj P. Gopalan .

Editor information

Editors and Affiliations

  1. Groupe ESEO, Angers, France

    Slimane Hammoudi

  2. Polytechnic Institute of Setúbal, Setúbal, Portugal & INSTICC, Setúbal, Portugal

    José Cordeiro

  3. Wroclaw University of Economics, Wroclaw, Poland & Macquarie University, Sydney, NSW, Australia

    Leszek A. Maciaszek

  4. Polytechnic Institute of Setúbal, Setúbal, Portugal & INSTICC, Setúbal, Poland

    Joaquim Filipe

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Rudra, A., Gopalan, R.P., Achuthan, N.R. (2014). Estimating Sufficient Sample Sizes for Approximate Decision Support Queries. In: Hammoudi, S., Cordeiro, J., Maciaszek, L., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2013. Lecture Notes in Business Information Processing, vol 190. Springer, Cham. https://doi.org/10.1007/978-3-319-09492-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-09492-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09491-5

  • Online ISBN: 978-3-319-09492-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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