论文信息 - Regression Based Algorithm for Optimizing Top-K Selection in Simulation Query Language

Regression Based Algorithm for Optimizing Top-K Selection in Simulation Query Language

In this paper we propose an algorithm for optimizing simulation budget allocation while minimizing the total processing cost for top-k queries. We also implement this algorithm as part of SimQL: an extension of SQL that includes probability functions expressed through stochastic simulation.

Chun-Hung Chen | Alexander Brodsky | Susan Farley

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