Artificial Intelligence and Decision Making
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Artificial Intelligence and Decision Making, 2024, Issue 1, Pages 79–91
DOI: https://doi.org/10.14357/20718594240107
(Mi iipr7)
 

Intelligent planning and control

Building a technological landscape of innovative solutions based on data mining

V. A. Shiboldenkov, D. M. Kkhan

N. E. Bauman Moscow State Technical University, Moscow, Russia
Abstract: The relevance of the research is determined by the development of digital technologies and digital transformation of enterprises. The complexity is the modeling of business automation and innovation management, which requires methods of applied research management and consideration of technology readiness. The purpose of this paper is to build an automation model in Orange analytical and simulation modeling environment. Descriptive methods, analytical methods and data discovery methods are used to achieve the research objective. The object of the study is the approaches to building a technology landscape. The subject of the study is the analysis of models and tools based on data mining. The result of the study is the developed model of automation based on intelligent data analysis, realized in the form of a conceptual scheme, which can be used to build a technological landscape of innovative solutions at enterprises and conclusions are made about the feasibility of using this model.
Keywords: technology landscape, intelligent analysis, knowledge-intensive business, automation, technology readiness, digital transformation.
Document Type: Article
Language: Russian
Citation: V. A. Shiboldenkov, D. M. Kkhan, “Building a technological landscape of innovative solutions based on data mining”, Artificial Intelligence and Decision Making, 2024, no. 1, 79–91
Citation in format AMSBIB
\Bibitem{ShiKkh24}
\by V.~A.~Shiboldenkov, D.~M.~Kkhan
\paper Building a technological landscape of innovative solutions based on data mining
\jour Artificial Intelligence and Decision Making
\yr 2024
\issue 1
\pages 79--91
\mathnet{http://mi.mathnet.ru/iipr7}
\crossref{https://doi.org/10.14357/20718594240107}
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