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A Regression Dependent Iterative Algorithm for Optimizing Top-K Selection in Simulation Query Language

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  • Susan Farley

    (George Mason University, USA)

  • Alexander Brodsky

    (George Mason University, USA)

  • Chun-Hung Chen

    (National Taiwan University, Taiwan)

Abstract

In this paper the authors propose an extension of the algorithm General Optimal Regression Budget Allocation ScHeme (GORBASH) for iteratively optimizing simulation budget allocation while minimizing the total processing cost for top-k queries. They also implement this algorithm as part of SimQL: an extension of SQL that includes probability functions expressed through stochastic simulation.

Suggested Citation

  • Susan Farley & Alexander Brodsky & Chun-Hung Chen, 2012. "A Regression Dependent Iterative Algorithm for Optimizing Top-K Selection in Simulation Query Language," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 4(3), pages 12-24, July.
    • Handle: RePEc:igg:jdsst0:v:4:y:2012:i:3:p:12-24
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