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Data mining approach for modeling risk assessment in computational grid

The result's identifiers

  • Result code in 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>

  • Result on the web

    <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>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Data mining approach for modeling risk assessment in computational grid

  • Original language description

    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

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2015

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Smart Innovation, Systems and Technologies. Volume 33

  • ISBN

    978-81-322-2201-9

  • ISSN

    2190-3018

  • e-ISSN

  • Number of pages

    12

  • Pages from-to

    673-684

  • Publisher name

    Springer

  • Place of publication

    New Delhi

  • Event location

    Sambalpur

  • Event date

    Dec 20, 2014

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article