INFORMATION TECHNOLOGY OF DIAGNOSIS MODELS SYNTHESIS BASED ON PARALLEL COMPUTING

Context. The problem of diagnosis models synthesis in the big data processing based on parallel computing is solved. The object of the research is the process of diagnosis models synthesis. The subject of the research are the methods and information technologies for diagnosis models synthesis. Objective. The research objective is to develop diagnosis models synthesis information technology. Method. The paper deals with information technology of diagnosis models synthesis which is a set of diagrams graphically describing structural elements of the system as well as the behavioral aspects of their interaction at various stages of diagnostics objects models construction. The developed information technology enables to perform the construction of distributed diagnostics systems where computationally complex stages of diagnosis models synthesis are performed on high-performance server equipment, which makes it possible to significantly increase the practical threshold for using diagnostics systems in the processing of big data sets for solving of the tasks of training sample data reduction, rules extraction, diagnosis models construction and retraining. Results. The software which implements the proposed information technology and allows to synthesize diagnosis models based on the given data samples has been developed. Conclusions. The conducted experiments have confirmed the proposed information technology operability and allow to recommend it for solving the problems of big data processing for technical and biomedical diagnostics in practice. The prospects for further researches may include the modification of the developed information technology by introducing of other methods of diagnosis models synthesis.

[1]  Andrii O. Oliinyk,et al.  Using parallel random search to train fuzzy neural networks , 2014, Automatic Control and Computer Sciences.

[2]  Yung C. Shin,et al.  Intelligent Systems: Modeling, Optimization, and Control , 2008 .

[3]  Qing Li,et al.  Modeling and Analysis of Enterprise and Information Systems: From Requirements to Realization , 2009 .

[4]  Tobias Friedrich,et al.  Genetic and Evolutionary Computation , 2015, Theoretical Computer Science.

[5]  Andrii Oliinyk,et al.  Individual prediction of the hypertensive patient condition based on computational intelligence , 2015, 2015 International Conference on Information and Digital Technologies.

[6]  Michael Stonebraker,et al.  A comparison of approaches to large-scale data analysis , 2009, SIGMOD Conference.

[7]  Khashayar Khorasani,et al.  Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach , 2009 .

[8]  Sergey Subbotin,et al.  The Sample and Instance Selection for Data Dimensionality Reduction , 2016, ICONS 2016.

[9]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , 2009, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines.

[10]  Yevgeniy V. Bodyanskiy,et al.  Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification , 2013, Inf. Sci..

[11]  Jeffrey Xu Yu,et al.  Keyword search in databases: the power of RDBMS , 2009, SIGMOD Conference.

[12]  William L. Goffe,et al.  Multi-core CPUs, Clusters, and Grid Computing: A Tutorial , 2005 .

[13]  S. Esakkirajan,et al.  Fundamentals of relational database management systems , 2007 .

[14]  Miroslaw Malek,et al.  A survey of online failure prediction methods , 2010, CSUR.

[15]  A. A. Oliinyk,et al.  THE MODEL FOR ESTIMATION OF COMPUTER SYSTEM USED RESOURCES WHILE EXTRACTING PRODUCTION RULES BASED ON PARALLEL COMPUTATIONS , 2017 .

[16]  Sing-Tze Bow,et al.  Pattern recognition and image preprocessing , 1992 .

[17]  Sergey Subbotin,et al.  The Dimensionality Reduction Methods Based on Computational Intelligence in Problems of Object Classification and Diagnosis , 2016, ICONS 2016.

[18]  Chris Price Computer-Based Diagnostic Systems , 1999, Practitioner Series.

[19]  A. A. Oliinyk PRODUCTION RULES EXTRACTION BASED ON NEGATIVE SELECTION , 2015 .

[20]  Adriane Chapman,et al.  Making database systems usable , 2007, SIGMOD '07.

[21]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[22]  Sergey Subbotin,et al.  Parallel Computer System Resource Planning for Synthesis of Neuro-Fuzzy Networks , 2016, ICONS 2016.

[23]  Vadym Shkarupylo,et al.  PARALLEL MULTIAGENT METHOD OF BIG DATA REDUCTION FOR PATTERN RECOGNITION , 2017 .

[24]  A. A. Oliinyk,et al.  The decision tree construction based on a stochastic search for the neuro-fuzzy network synthesis , 2015, Optical Memory and Neural Networks.

[25]  Denis Kelliher,et al.  An object-oriented architecture for extensible structural design software , 2012 .

[26]  A. A. Oliinyk,et al.  A stochastic approach for association rule extraction , 2016, Pattern Recognition and Image Analysis.

[27]  Zaigham Mahmood Data Science and Big Data Computing: Frameworks and Methodologies , 2016 .

[28]  Geoffrey I. Webb,et al.  Encyclopedia of Machine Learning , 2011, Encyclopedia of Machine Learning.

[29]  Andrii O. Oliinyk,et al.  Experimental investigation with analyzing the training method complexity of neuro-fuzzy networks based on parallel random search , 2015, Automatic Control and Computer Sciences.

[30]  Ajith Abraham,et al.  Swarm Intelligence in Data Mining , 2009, Swarm Intelligence in Data Mining.

[31]  Hassan Gomaa,et al.  Designing Software Product Lines with UML 2.0: From Use Cases to Pattern-Based Software Architectures , 2006, 10th International Software Product Line Conference (SPLC'06).

[32]  Jeff A. Stuart,et al.  A study of Persistent Threads style GPU programming for GPGPU workloads , 2012, 2012 Innovative Parallel Computing (InPar).

[33]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[34]  Mitsuo Gen,et al.  Genetic algorithms and engineering design , 1997 .

[35]  Richard N. Taylor,et al.  Software Design and Architecture The once and future focus of software engineering , 2007, Future of Software Engineering (FOSE '07).

[36]  Helon Vicente Hultmann Ayala,et al.  Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks , 2016 .

[37]  A. A. Oliinyk,et al.  PARALLEL COMPUTING SYSTEM RESOURCES PLANNING FOR NEURO-FUZZY MODELS SYNTHESIS AND BIG DATA PROCESSING , 2017 .