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Ontologies to Reduce Uncertainty in R&D Project Planning. / Stoianova, Olga V. ; Moskaleva, Valeriia D. .

Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). ed. / Sergey Kovalev; Valery Tarassov; Vaclav Snasel; Andrey Sukhanov. Springer Nature, 2022. p. 370-379 (Lecture Notes in Networks and Systems; Vol. 330).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Stoianova, OV & Moskaleva, VD 2022, Ontologies to Reduce Uncertainty in R&D Project Planning. in S Kovalev, V Tarassov, V Snasel & A Sukhanov (eds), Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). Lecture Notes in Networks and Systems, vol. 330, Springer Nature, pp. 370-379, 5th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2021, Sochi, Russian Federation, 30/09/21. https://doi.org/10.1007/978-3-030-87178-9_37

APA

Stoianova, O. V., & Moskaleva, V. D. (2022). Ontologies to Reduce Uncertainty in R&D Project Planning. In S. Kovalev, V. Tarassov, V. Snasel, & A. Sukhanov (Eds.), Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21) (pp. 370-379). (Lecture Notes in Networks and Systems; Vol. 330). Springer Nature. https://doi.org/10.1007/978-3-030-87178-9_37

Vancouver

Stoianova OV, Moskaleva VD. Ontologies to Reduce Uncertainty in R&D Project Planning. In Kovalev S, Tarassov V, Snasel V, Sukhanov A, editors, Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). Springer Nature. 2022. p. 370-379. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-3-030-87178-9_37

Author

Stoianova, Olga V. ; Moskaleva, Valeriia D. . / Ontologies to Reduce Uncertainty in R&D Project Planning. Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). editor / Sergey Kovalev ; Valery Tarassov ; Vaclav Snasel ; Andrey Sukhanov. Springer Nature, 2022. pp. 370-379 (Lecture Notes in Networks and Systems).

BibTeX

@inproceedings{d377fd64ff0440ab8625052d174e72aa,
title = "Ontologies to Reduce Uncertainty in R&D Project Planning",
abstract = "R&D projects often fail to meet predetermined deadlines and budgets, which is due not only to poor organization of the research and development process, but also to the complexity of performance and efficiency evaluation. The evaluation of effectiveness is complicated by the fact that the final result is made up of a sequence of other results. In assessing effectiveness, a significant share of uncertainty lies in the evaluation of labor costs. The aim of the study is to develop tools to reduce information uncertainty and improve the validity of planning decisions for R&D projects. The paper presents ontologies reflecting relationships between R&D processes and activities within processes with R&D results, as well as relationships between R&D results. The purpose of these ontologies is to evaluate the individual stages of the process in terms of performance; to determine the authorship of individual results and, accordingly, to evaluate the work of employees. Models in the form of ontologies and proposed knowledge extraction procedures serve as the basis for the decision support system for R&D project management. With the help of this system the formation of project teams and the allocation of resources to project tasks can be carried out when resolving resource conflicts.",
keywords = "Ontology, Research and Development (R&D), Project planning, Process evaluation, Ontology, Research and development, Intellectual property, Project planning, Process evaluation",
author = "Stoianova, {Olga V.} and Moskaleva, {Valeriia D.}",
note = "Stoianova O.V., Moskaleva V.D. (2022) Ontologies to Reduce Uncertainty in R&D Project Planning. In: Kovalev S., Tarassov V., Snasel V., Sukhanov A. (eds) Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI{\textquoteright}21). IITI 2021. Lecture Notes in Networks and Systems, vol 330. Springer, Cham. https://proxy.library.spbu.ru:2060/10.1007/978-3-030-87178-9_37; 5th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2021 ; Conference date: 30-09-2021 Through 04-10-2021",
year = "2022",
doi = "10.1007/978-3-030-87178-9_37",
language = "English",
isbn = "9783030871772",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "370--379",
editor = "Sergey Kovalev and Valery Tarassov and Vaclav Snasel and Andrey Sukhanov",
booktitle = "Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI{\textquoteright}21)",
address = "Germany",

}

RIS

TY - GEN

T1 - Ontologies to Reduce Uncertainty in R&D Project Planning

AU - Stoianova, Olga V.

AU - Moskaleva, Valeriia D.

N1 - Stoianova O.V., Moskaleva V.D. (2022) Ontologies to Reduce Uncertainty in R&D Project Planning. In: Kovalev S., Tarassov V., Snasel V., Sukhanov A. (eds) Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21). IITI 2021. Lecture Notes in Networks and Systems, vol 330. Springer, Cham. https://proxy.library.spbu.ru:2060/10.1007/978-3-030-87178-9_37

PY - 2022

Y1 - 2022

N2 - R&D projects often fail to meet predetermined deadlines and budgets, which is due not only to poor organization of the research and development process, but also to the complexity of performance and efficiency evaluation. The evaluation of effectiveness is complicated by the fact that the final result is made up of a sequence of other results. In assessing effectiveness, a significant share of uncertainty lies in the evaluation of labor costs. The aim of the study is to develop tools to reduce information uncertainty and improve the validity of planning decisions for R&D projects. The paper presents ontologies reflecting relationships between R&D processes and activities within processes with R&D results, as well as relationships between R&D results. The purpose of these ontologies is to evaluate the individual stages of the process in terms of performance; to determine the authorship of individual results and, accordingly, to evaluate the work of employees. Models in the form of ontologies and proposed knowledge extraction procedures serve as the basis for the decision support system for R&D project management. With the help of this system the formation of project teams and the allocation of resources to project tasks can be carried out when resolving resource conflicts.

AB - R&D projects often fail to meet predetermined deadlines and budgets, which is due not only to poor organization of the research and development process, but also to the complexity of performance and efficiency evaluation. The evaluation of effectiveness is complicated by the fact that the final result is made up of a sequence of other results. In assessing effectiveness, a significant share of uncertainty lies in the evaluation of labor costs. The aim of the study is to develop tools to reduce information uncertainty and improve the validity of planning decisions for R&D projects. The paper presents ontologies reflecting relationships between R&D processes and activities within processes with R&D results, as well as relationships between R&D results. The purpose of these ontologies is to evaluate the individual stages of the process in terms of performance; to determine the authorship of individual results and, accordingly, to evaluate the work of employees. Models in the form of ontologies and proposed knowledge extraction procedures serve as the basis for the decision support system for R&D project management. With the help of this system the formation of project teams and the allocation of resources to project tasks can be carried out when resolving resource conflicts.

KW - Ontology

KW - Research and Development (R&D)

KW - Project planning

KW - Process evaluation

KW - Ontology

KW - Research and development

KW - Intellectual property

KW - Project planning

KW - Process evaluation

UR - http://www.scopus.com/inward/record.url?scp=85115830634&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/119bf7c0-9dc6-3e99-8f93-ec40f36c59db/

U2 - 10.1007/978-3-030-87178-9_37

DO - 10.1007/978-3-030-87178-9_37

M3 - Conference contribution

SN - 9783030871772

T3 - Lecture Notes in Networks and Systems

SP - 370

EP - 379

BT - Proceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21)

A2 - Kovalev, Sergey

A2 - Tarassov, Valery

A2 - Snasel, Vaclav

A2 - Sukhanov, Andrey

PB - Springer Nature

T2 - 5th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2021

Y2 - 30 September 2021 through 4 October 2021

ER -

ID: 86574303