Context. The task of production rules extraction while processing big arrays of data has been discussed. The problem of estimation of<br />computer system used resources while extracting production rules based on parallel computations has been solved. The research object is the<br />process of production rules extraction. The research subject lies in methods of parallel computer systems’ resource planning.<br />Objective. The purpose of the work is а construction of the model for estimation parallel computer systems resources used to solve<br />applied problems based on the parallel method of production rules extraction.<br />Method. The article deals with the model building of used resources estimation of parallel computer system while extracting production<br />rules. The model for estimation of computer system used resources while executing the parallel method of method of production rules<br />extraction is proposed. Synthesized model takes into account the type of computer system, the amount of processors involved to solving the<br />task and the bandwidth of data transfer network. In addition, the model considers parameters of used mathematical equipment (the portions<br />of parallel system nodes involved for production rules extraction based on decision trees, associative rules and negative selection). Also the<br />parameters of solved application task are taken into account. They are the number of observations and the number of characteristics in a given<br />set of data describing the results of observations of the object or process being studied. The synthesized neural model is a polyalgorithmic. It<br />allows estimating two characteristics of parallel computer system while executing the parallel method of production rules extraction. The first<br />one is time used. And the second one is the volume of memory used.<br />Results. The software which implements the proposed model and allows predicting the time and the volume of memory used of parallel<br />computer system while solving practice tasks has been developed.<br />Conclusions. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in<br />practice for solving the problems of big data processing. The prospects for further research may include the creation of parallel methods for<br />feature selection, as well as an experimental study of proposed model on more complex practical problems of different nature and dimensionality.
Науковий журнал «Радіоелектроніка, інформатика, управління»
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Oliinyk, A.A.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine Skrupsky, S. Yu.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine Shkarupylo, V. V.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine Subbotin, S. A.; Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine |
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Context. The task of production rules extraction while processing big arrays of data has been discussed. The problem of estimation of<br />computer system used resources while extracting production rules based on parallel computations has been solved. The research object is the<br />process of production rules extraction. The research subject lies in methods of parallel computer systems’ resource planning.<br />Objective. The purpose of the work is а construction of the model for estimation parallel computer systems resources used to solve<br />applied problems based on the parallel method of production rules extraction.<br />Method. The article deals with the model building of used resources estimation of parallel computer system while extracting production<br />rules. The model for estimation of computer system used resources while executing the parallel method of method of production rules<br />extraction is proposed. Synthesized model takes into account the type of computer system, the amount of processors involved to solving the<br />task and the bandwidth of data transfer network. In addition, the model considers parameters of used mathematical equipment (the portions<br />of parallel system nodes involved for production rules extraction based on decision trees, associative rules and negative selection). Also the<br />parameters of solved application task are taken into account. They are the number of observations and the number of characteristics in a given<br />set of data describing the results of observations of the object or process being studied. The synthesized neural model is a polyalgorithmic. It<br />allows estimating two characteristics of parallel computer system while executing the parallel method of production rules extraction. The first<br />one is time used. And the second one is the volume of memory used.<br />Results. The software which implements the proposed model and allows predicting the time and the volume of memory used of parallel<br />computer system while solving practice tasks has been developed.<br />Conclusions. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in<br />practice for solving the problems of big data processing. The prospects for further research may include the creation of parallel methods for<br />feature selection, as well as an experimental study of proposed model on more complex practical problems of different nature and dimensionality. |
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Zaporizhzhya National Technical University 2017-05-13 11:57:04 |
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application/pdf http://ric.zntu.edu.ua/article/view/101462 |
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Radio Electronics, Computer Science, Control; No 1 (2017): Radio Electronics, Computer Science, Control |
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Copyright (c) 2017 A.A. Oliinyk, S. Yu. Skrupsky, V. V. Shkarupylo, S. A. Subbotin |
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