Data mining approach for modeling risk assessment in computational grid

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86097028" target="_blank" >RIV/61989100:27240/15:86097028 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-81-322-2202-6_61" target="_blank" >http://dx.doi.org/10.1007/978-81-322-2202-6_61</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-81-322-2202-6_61" target="_blank" >10.1007/978-81-322-2202-6_61</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data mining approach for modeling risk assessment in computational grid

Popis výsledku v původním jazyce

Assessing Risk in a computational grid environment is an essential need for a user who runs applications from a remote machine on the grid, where resource sharing is the main concern. As Grid computing is the ultimate solution believed to meet the ever-expanding computational needs of organizations, analysis of the various possible risks to evaluate and develop solutions to resolve these risks is needed. For correctly predicting the risk environment, we made a comparative analysis of various machine learning modeling methods on a dataset of risk factors. First we conducted an online survey with international experts about the various risk factors associated with grid computing. Second we assigned numerical ranges to each risk factor based on a genericgrid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process. The empirical results illustrate that the proposed framework is able to provide risk a

Název v anglickém jazyce

Data mining approach for modeling risk assessment in computational grid

Popis výsledku anglicky

Assessing Risk in a computational grid environment is an essential need for a user who runs applications from a remote machine on the grid, where resource sharing is the main concern. As Grid computing is the ultimate solution believed to meet the ever-expanding computational needs of organizations, analysis of the various possible risks to evaluate and develop solutions to resolve these risks is needed. For correctly predicting the risk environment, we made a comparative analysis of various machine learning modeling methods on a dataset of risk factors. First we conducted an online survey with international experts about the various risk factors associated with grid computing. Second we assigned numerical ranges to each risk factor based on a genericgrid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process. The empirical results illustrate that the proposed framework is able to provide risk a

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Smart Innovation, Systems and Technologies. Volume 33

ISBN

978-81-322-2201-9

ISSN

2190-3018

e-ISSN

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Počet stran výsledku

12

Strana od-do

673-684

Název nakladatele

Springer

Místo vydání

New Delhi

Místo konání akce

Sambalpur

Datum konání akce

20. 12. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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