DOI:https://doi.org/10.15407/kvt208.03.021
Cybernetics and Computer Engineering, 2022, 3(209)
STEPASHKO V.S., DSc (Engineering), Prof.,
Head of the Department of
Information Technologies of Inductive Modeling
https://orcid.org/0000-0001-7882-3208
e-mail: stepashko@irtc.org.ua
SAVCHENKO-SYNIAKOVA Ye.А., PhD (Engineering),
Senior Researcher,
the Department of Information Technologies of Inductive Modeling,
https://orcid.org/0000-0003-4851-9664
e-mail: savchenko_e@meta.ua
PIDNEBESNA H.А., PhD (Engineering), Researcher,
the Department of Information Technologies of Inductive Modeling
https://orcid.org/0000-0002-5735-9861
e-mail: pidnebesna@ukr.net
International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine.
40, Akad. Hlushkov av., Kyiv, 03187, Ukraine
PROBLEM OF CONSTRUCTING AN ONTOLOGICAL METAMODEL OF ITERATIVE ITIRATIVE GROUP METHOD OF DATA HANDLING ALGORITHMS
Introduction. Data volumes are permanently increasing and some new approaches are needed for storage and processing them considering the development and improvement of modern computers. This puts forward new requirements to automatic data processing tools and intelligent systems for analyzing information with taking into account its semantics.
The advantage of iterative GMDH algorithms is that they are able to work with a large number of arguments. The generalized iterative GMDH algorithm includes various former modifications of these algorithms. For example, algorithms of multilayer and relaxation types as well as varieties of iterative-combinatorial (hybrid) algorithms are diverse particular cases of the generalized one.
Metamodeling is the construction of generalized models of a certain group of objects (software tools, mathematical models, information systems). An ontological metamodel of the iterative GMDH algorithms was built using the Protege tools in order to structure knowledge in this subject area.
The purpose of the paper is to analyze the developed iterative GMDH algorithms and propose an approach to structuring knowledge оn iterative GMDH algorithms by building an ontological metamodel of this subject area.
Results. A retrospective analysis of the developed iterative GMDH algorithms іs carried out in the paper, their advantages and disadvantages are indicated. It is shown that the generalized iterative algorithm, whose special cases are both known and new varieties of multilayer, relaxation and iterative-combinatorial GMDH algorithms, makes it possible to compare the effectiveness of various algorithms and solve real modeling problems. Based on the results of this study, an ontological metamodel of iterative GMDH algorithms has been developed.
Conclusions. The advantage of iterative GMDH algorithms is that they allow processing big data sets. The generalized iterative algorithm allows forming typical architectures of previously developed modifications of these algorithms when setting up various operating modes of this algorithm. The construction of an ontological metamodel based on this one allows structuring knowledge on the available iterative algorithms making it possible to automate the design and use of specialized software tools for specific applied tasks.
Keywords: inductive modeling, GMDH, iterative algorithms, mathematical model, metamodeling, subject area, ontology
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Received 23.06.2022
HOW TO CITE:
Stepashko V.S., Savchenko-Syniakova Ye.А., Pidnebesna H.А. Problem of Constructing an Ontological Metamodel of Itirative Group Method of Data Handling Algorithms. Cybernetics and Computer Engineering, 2022, no 3(209), pp. 21-33.
https://doi.org/10.15407/kvt208.03.021