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INTELLECTUAL GRAPH MODELS for RELATED DATA PROCESSING. / Arseniev, D.; Baskakov, D.; Shkodyrev, V.; Арсеньев, Дмитрий Германович.

в: Journal of Physics: Conference Series, Том 1864, № 1, 012077, 20.05.2021.

Результаты исследований: Научные публикации в периодических изданиях › статья в журнале по материалам конференции › Рецензирование

Harvard

Arseniev, D, Baskakov, D, Shkodyrev, V & Арсеньев, ДГ 2021, 'INTELLECTUAL GRAPH MODELS for RELATED DATA PROCESSING', Journal of Physics: Conference Series, Том. 1864, № 1, 012077. https://doi.org/10.1088/1742-6596/1864/1/012077

APA

Arseniev, D., Baskakov, D., Shkodyrev, V., & Арсеньев, Д. Г. (2021). INTELLECTUAL GRAPH MODELS for RELATED DATA PROCESSING. Journal of Physics: Conference Series, 1864(1), [012077]. https://doi.org/10.1088/1742-6596/1864/1/012077

Vancouver

Arseniev D, Baskakov D, Shkodyrev V, Арсеньев ДГ. INTELLECTUAL GRAPH MODELS for RELATED DATA PROCESSING. Journal of Physics: Conference Series. 2021 Май 20;1864(1). 012077. https://doi.org/10.1088/1742-6596/1864/1/012077

Author

Arseniev, D. ; Baskakov, D. ; Shkodyrev, V. ; Арсеньев, Дмитрий Германович. / INTELLECTUAL GRAPH MODELS for RELATED DATA PROCESSING. в: Journal of Physics: Conference Series. 2021 ; Том 1864, № 1.

BibTeX

@article{5411b54526394b4f843afc8b7980cbd7,
title = "INTELLECTUAL GRAPH MODELS for RELATED DATA PROCESSING",
abstract = "In connection with the wide spreading of various intelligent sensors, IoT devices, smartphones, autonomous transport systems, various industrial and home automation systems, an unprecedented amount of data is generated, including those intelligently linked to each other. Linked data allows you to build complex and varied relationships between objects and subjects of the real world. Unfortunately, modern big data processing systems and machine learning models are extremely poorly suited for working with such dynamically linked data, especially in the case of real-time systems. We discuss current and future-proof approaches to working with such data using graph analysis models.",
author = "D. Arseniev and D. Baskakov and V. Shkodyrev and Арсеньев, {Дмитрий Германович}",
note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 13th Multiconference on Control Problems, MCCP 2020 ; Conference date: 06-10-2020 Through 08-10-2020",
year = "2021",
month = may,
day = "20",
doi = "10.1088/1742-6596/1864/1/012077",
language = "English",
volume = "1864",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",
url = "http://www.elektropribor.spb.ru/nauchnaya-deyatelnost/xiii-mkpu/index3.php",

}

RIS

TY - JOUR

T1 - INTELLECTUAL GRAPH MODELS for RELATED DATA PROCESSING

AU - Arseniev, D.

AU - Baskakov, D.

AU - Shkodyrev, V.

AU - Арсеньев, Дмитрий Германович

N1 - Conference code: 13

PY - 2021/5/20

Y1 - 2021/5/20

N2 - In connection with the wide spreading of various intelligent sensors, IoT devices, smartphones, autonomous transport systems, various industrial and home automation systems, an unprecedented amount of data is generated, including those intelligently linked to each other. Linked data allows you to build complex and varied relationships between objects and subjects of the real world. Unfortunately, modern big data processing systems and machine learning models are extremely poorly suited for working with such dynamically linked data, especially in the case of real-time systems. We discuss current and future-proof approaches to working with such data using graph analysis models.

AB - In connection with the wide spreading of various intelligent sensors, IoT devices, smartphones, autonomous transport systems, various industrial and home automation systems, an unprecedented amount of data is generated, including those intelligently linked to each other. Linked data allows you to build complex and varied relationships between objects and subjects of the real world. Unfortunately, modern big data processing systems and machine learning models are extremely poorly suited for working with such dynamically linked data, especially in the case of real-time systems. We discuss current and future-proof approaches to working with such data using graph analysis models.

UR - http://www.scopus.com/inward/record.url?scp=85107436106&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1864/1/012077

DO - 10.1088/1742-6596/1864/1/012077

M3 - Conference article

AN - SCOPUS:85107436106

VL - 1864

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

SN - 1742-6588

IS - 1

M1 - 012077

T2 - 13th Multiconference on Control Problems, MCCP 2020

Y2 - 6 October 2020 through 8 October 2020

ER -

ID: 86501616