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

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

Susan Farley, Alexander Brodsky, Chun-Hung Chen
Copyright: © 2012 |Volume: 4 |Issue: 3 |Pages: 13
ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781466611535|DOI: 10.4018/jdsst.2012070102
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MLA

Farley, Susan, et al. "A Regression Dependent Iterative Algorithm for Optimizing Top-K Selection in Simulation Query Language." IJDSST vol.4, no.3 2012: pp.12-24. http://doi.org/10.4018/jdsst.2012070102

APA

Farley, S., Brodsky, A., & Chen, C. (2012). A Regression Dependent Iterative Algorithm for Optimizing Top-K Selection in Simulation Query Language. International Journal of Decision Support System Technology (IJDSST), 4(3), 12-24. http://doi.org/10.4018/jdsst.2012070102

Chicago

Farley, Susan, Alexander Brodsky, and Chun-Hung Chen. "A Regression Dependent Iterative Algorithm for Optimizing Top-K Selection in Simulation Query Language," International Journal of Decision Support System Technology (IJDSST) 4, no.3: 12-24. http://doi.org/10.4018/jdsst.2012070102

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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.

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