9783330327764 - Modelling Risk Assessment in Computational Grid de Abdelwahab, Sara; Abraham, Ajith (6 resultados)

Taschenbuch. Condición: Neu. Modelling Risk Assessment in Computational Grid | Sara Abdelwahab (u. a.) | Taschenbuch | 160 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330327764 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condición: Brand New. 160 pages. 8.66x5.91x0.37 inches. In Stock.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 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 a 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 generic grid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process. Finally, we modeled the prediction process of risk assessment in grid computing utilizing Meta learning approaches in order to improve the performance of the individual predictive models. We concluded that data mining tools can provide further steps in building a risk assessment model in a Grid environment with good accuracy, according to the obtained empirical results. 160 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abdelwahab SaraDr. Sara Abdelwahab works in the field of risk assessment and grid computing using different machine learning approaches. Dr. Ajith Abraham works in the field of machine intelligence and is the current director of Mach.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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. 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 a 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 generic grid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process. Finally, we modeled the prediction process of risk assessment in grid computing utilizing Meta learning approaches in order to improve the performance of the individual predictive models. We concluded that data mining tools can provide further steps in building a risk assessment model in a Grid environment with good accuracy, according to the obtained empirical results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 160 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. 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 a 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 generic grid environment. We utilized data mining tools to pick the contributing attributes that improve the quality of the risk assessment prediction process. Finally, we modeled the prediction process of risk assessment in grid computing utilizing Meta learning approaches in order to improve the performance of the individual predictive models. We concluded that data mining tools can provide further steps in building a risk assessment model in a Grid environment with good accuracy, according to the obtained empirical results.