Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Designing a Decision Support System for Predicting Innovation Activity. / Korableva, Olga N. ; Mityakova, Viktoriya N. ; Kalimullina, Olga V. .
ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems. ред. / Joaquim Filipe; Michal Smialek; Alexander Brodsky; Slimane Hammoudi. Том 1 Portugal : SciTePress, 2020. стр. 619-625.Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Designing a Decision Support System for Predicting Innovation Activity
AU - Korableva, Olga N.
AU - Mityakova, Viktoriya N.
AU - Kalimullina, Olga V.
PY - 2020
Y1 - 2020
N2 - Decision support systems for predicting innovation activity at the macro level are not yet widely used, and the authors have not been able to find direct analogues of such a system. The relevance of creating the system is due to the need to take into account heterogeneous structured and unstructured information, including in natural language, when predicting innovation activity. The article describes the process of designing a decision support system for predicting innovation activity, based on the system for integrating macroeconomic and statistical data (described by the authors in previous articles) by adding a module of decision-making methods. The UML diagram of use cases and the UML diagram of the components of this module, the general architecture of the prototype of the decision support system, are presented. It also describes an algorithm for predicting innovation activity and its impact on the potential for economic growth using DSS.
AB - Decision support systems for predicting innovation activity at the macro level are not yet widely used, and the authors have not been able to find direct analogues of such a system. The relevance of creating the system is due to the need to take into account heterogeneous structured and unstructured information, including in natural language, when predicting innovation activity. The article describes the process of designing a decision support system for predicting innovation activity, based on the system for integrating macroeconomic and statistical data (described by the authors in previous articles) by adding a module of decision-making methods. The UML diagram of use cases and the UML diagram of the components of this module, the general architecture of the prototype of the decision support system, are presented. It also describes an algorithm for predicting innovation activity and its impact on the potential for economic growth using DSS.
KW - Decision support systems
KW - innovation activity
KW - Potential of Economic Growth
KW - ontology
KW - Semantic Search
KW - Ontology
KW - Decision Support System
KW - Innovation Activity
UR - https://www.scitepress.org/PublicationsDetail.aspx?ID=4PsDTjuKj/Y=&t=1
UR - http://www.scopus.com/inward/record.url?scp=85090770411&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9789897584237
VL - 1
SP - 619
EP - 625
BT - ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems
A2 - Filipe, Joaquim
A2 - Smialek, Michal
A2 - Brodsky, Alexander
A2 - Hammoudi, Slimane
PB - SciTePress
CY - Portugal
T2 - 22nd International Conference on Enterprise Information Systems
Y2 - 5 May 2020 through 7 May 2020
ER -
ID: 53730761