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

Autor: Alexander Brodsky, Susan Farley, Chun-Hung Chen
Rok vydání: 2012
Předmět:
Zdroj: International Journal of Decision Support System Technology (IJDSST). 4(3):12-24
DOI: 10.4018/jdsst.2012070102
Popis: 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.
Databáze: OpenAIRE