DocumentCode
573532
Title
Regression Based Algorithm for Optimizing Top-K Selection in Simulation Query Language
Author
Farley, Susan ; Brodsky, Alexander ; Chen, Chun-Hung
Author_Institution
Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA
fYear
2012
fDate
1-5 April 2012
Firstpage
103
Lastpage
110
Abstract
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.
Keywords
SQL; digital simulation; query languages; regression analysis; stochastic processes; SQL extension; SimQL; budget allocation simulation; optimizing top-k selection; probability functions; regression based algorithm; simulation query language; stochastic simulation; top-k queries; Computational modeling; Data models; Databases; Java; Monte Carlo methods; Probabilistic logic; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Engineering Workshops (ICDEW), 2012 IEEE 28th International Conference on
Conference_Location
Arlington, VA
Print_ISBN
978-1-4673-1640-8
Type
conf
DOI
10.1109/ICDEW.2012.65
Filename
6313665
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