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Visual Models For Big Data Analysis. / Gavrilova, T.; Gladkova, M.

Proceedings of the Symposium Automated Systems and Technologies AST 2015. Издательство Санкт-Петербургского Государственного Политехнического Университета, 2015. p. 59-67.

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

Harvard

Gavrilova, T & Gladkova, M 2015, Visual Models For Big Data Analysis. in Proceedings of the Symposium Automated Systems and Technologies AST 2015. Издательство Санкт-Петербургского Государственного Политехнического Университета, pp. 59-67.

APA

Gavrilova, T., & Gladkova, M. (2015). Visual Models For Big Data Analysis. In Proceedings of the Symposium Automated Systems and Technologies AST 2015 (pp. 59-67). Издательство Санкт-Петербургского Государственного Политехнического Университета.

Vancouver

Gavrilova T, Gladkova M. Visual Models For Big Data Analysis. In Proceedings of the Symposium Automated Systems and Technologies AST 2015. Издательство Санкт-Петербургского Государственного Политехнического Университета. 2015. p. 59-67

Author

Gavrilova, T. ; Gladkova, M. / Visual Models For Big Data Analysis. Proceedings of the Symposium Automated Systems and Technologies AST 2015. Издательство Санкт-Петербургского Государственного Политехнического Университета, 2015. pp. 59-67

BibTeX

@inproceedings{a2aafbf7b9d24f04bdeaaa9848c61b04,
title = "Visual Models For Big Data Analysis",
abstract = "The paper presents an approach for visual analysis and structuring of Big Data based on the principles of ontological engineering and cognitive psychology. Ontologies are to be used as a basis for big data volumes of information, we tried to follow the principle of good shape. The data structuring procedure is the key element of any model design and development. The suggested methodology proposes better vision and understanding of huge amounts of business information. Ontologies that describe the main concepts of exemplary domains are used both for deeper comprehension and better information sharing. The application of this methodology is considered for the business data on global entrepreneurship monitoring.",
keywords = "РИНЦ, Big Data Analysis, Visual Models, РИНЦ",
author = "T. Gavrilova and M. Gladkova",
note = "Gavrilova, T. Visual Models For Big Data Analysis / T. Gavrilova, M. Gladkova // Proceedings of the Symposium Automated Systems and Technologies AST 2015. - Санкт-Петербург : Издательство Санкт-Петербургского Государственного Политехнического Университета, 2015. - P. 59-67. ",
year = "2015",
language = "English",
pages = "59--67",
booktitle = "Proceedings of the Symposium Automated Systems and Technologies AST 2015",
publisher = "Издательство Санкт-Петербургского Государственного Политехнического Университета",
address = "Russian Federation",

}

RIS

TY - GEN

T1 - Visual Models For Big Data Analysis

AU - Gavrilova, T.

AU - Gladkova, M.

N1 - Gavrilova, T. Visual Models For Big Data Analysis / T. Gavrilova, M. Gladkova // Proceedings of the Symposium Automated Systems and Technologies AST 2015. - Санкт-Петербург : Издательство Санкт-Петербургского Государственного Политехнического Университета, 2015. - P. 59-67.

PY - 2015

Y1 - 2015

N2 - The paper presents an approach for visual analysis and structuring of Big Data based on the principles of ontological engineering and cognitive psychology. Ontologies are to be used as a basis for big data volumes of information, we tried to follow the principle of good shape. The data structuring procedure is the key element of any model design and development. The suggested methodology proposes better vision and understanding of huge amounts of business information. Ontologies that describe the main concepts of exemplary domains are used both for deeper comprehension and better information sharing. The application of this methodology is considered for the business data on global entrepreneurship monitoring.

AB - The paper presents an approach for visual analysis and structuring of Big Data based on the principles of ontological engineering and cognitive psychology. Ontologies are to be used as a basis for big data volumes of information, we tried to follow the principle of good shape. The data structuring procedure is the key element of any model design and development. The suggested methodology proposes better vision and understanding of huge amounts of business information. Ontologies that describe the main concepts of exemplary domains are used both for deeper comprehension and better information sharing. The application of this methodology is considered for the business data on global entrepreneurship monitoring.

KW - РИНЦ

KW - Big Data Analysis

KW - Visual Models

KW - РИНЦ

M3 - Conference contribution

SP - 59

EP - 67

BT - Proceedings of the Symposium Automated Systems and Technologies AST 2015

PB - Издательство Санкт-Петербургского Государственного Политехнического Университета

ER -

ID: 4789964