Open Access
E3S Web Conf.
Volume 263, 2021
XXIV International Scientific Conference “Construction the Formation of Living Environment” (FORM-2021)
Article Number 04036
Number of page(s) 9
Section Engineering and Smart Systems in Construction
Published online 28 May 2021
  1. G.V. Sopegin, D.B. Sursanov. Use of automated systems for monitoring of structures (ASMS). Vestnik MGSU, 2017.,V.12,2 (101) p.230–242, DOI: 10.22227/1997-0935.2017.2.230-242 [Google Scholar]
  2. Konikov A. Promising wireless applications in the construction industry. Web of Conferences 164, 10043 (2020) TPACEE-2019 [Google Scholar]
  3. Orlov S. Automation Design SCS//Magazine Network Solutions LAN, 2011, N7-8, 13010054 [Google Scholar]
  4. Urban Sensor Data Streams: London 2013 // IEEE Internet Computing. 2013. vol. 17 №6. p. 1. doi:10.1109/MIC.2013.85. [Google Scholar]
  5. Deakin, Mark. From Intelligent to Smart Cities // Journal of Intelligent Buildings International: From Intelligent Cities to Smart Cities. 2011. v. 3, № 3. doi:10.1080/17508975.2011.586671. [Google Scholar]
  6. Gooch, Daniel. Reimagining the Role of Citizens in Smart City Projects // Proceedings of the 2015 ACM International Symposium on Wearable Computers: ACM, 2015. vol1. p. 587–1594. doi:10.1145/2800835.2801622. [Google Scholar]
  7. Olivier Hersent, David Boswarthick, Omar Elloumi. The Internet of Things: Key Applications and Protocols. — Willey, 2012. - 370 p [Google Scholar]
  8. L. Chernyak. IoT platform. Open systems. DBMS, 2012. № 7. [Google Scholar]
  9. Alexei Lagutenkov. The quiet expansion of the integration of things/ /Science and Life. 2018. № 5. p. 38-42. [Google Scholar]
  10. Jack B. Reid and Donna H. Rhodes Digital System Models: An investigation of the non-technical challenges and research needs. 2016 Conference on Systems Engineering, Massachusetts Institute of Technology. [Google Scholar]
  11. Computer vision: technologies, market, perspectives. TADVISER. Government.Bisiness.IT. 2019. №6 [Google Scholar]
  12. Maximov K.V. The effectiveness of the use of cloud computing: methods and models of evaluation // Applied computer science, 2016. № 1(81), p.106–113. [Google Scholar]
  13. Alexandr Konikov, Ekaterina Kulikova and Olga Stifeeva. Research of the possibilities of application of the Data Warehouse in the construction area. MATEC Web of Conferences 251, 03062 (2018) [CrossRef] [EDP Sciences] [Google Scholar]
  14. Konikov A., Konikov G. Big Data is a powerful tool for improving the environment in the construction business. IOP Conference Series: Earth and Environmental Science, 2017, vol. 90, p. 012184. [Google Scholar]
  15. Konikov A.I. Promising areas in the field of information systems for construction management // Industrial and Civil Engineering, 2019, №6, p. 64–69 [Google Scholar]
  16. A.I. Konikov. Study of a number of aspects of using Big Data technology in constructionе, BST Journal, 2019, №2, p. 28–29. [Google Scholar]
  17. Nikolay Ivanov and Maxim Gnevanov. Big data: perspectives of using in urban planning and management. MAT EC Web of Conferences 170, 01107 (2018) [Google Scholar]
  18. Kurt Stockinger, Nils Bundi, Jonas Heitz and Wolfgang Breymann. Scalable architecture for Big Data financial analytics: user-defined functions vs. SQL. Journal of Big Data. March 2019. DOI 10.1186/s40537-019-0209-0 [PubMed] [Google Scholar]
  19. Gnevanov М. V., Ivanov N. A. Big Data technology - using in urban planning // Industrial and Civil Engineering, 2018. № 4. p. 83–87. [Google Scholar]
  20. Valpeters M., Kireev I., Ivanov N., 2018. Application of machine learning methods in big data analytics at management of contracts in the construction industry. MATEC Web of Conferences, 170, 01106 [CrossRef] [EDP Sciences] [Google Scholar]
  21. Ciresan, Dan; Meier, U.; Schmidhuber, J. Multi-column deep neural networks for image classification // 2012 IEEE Conference on Computer Vision and Pattern Recognition: journal. 2012. June. p. 3642–3649. doi:10.1109/cvpr.2012.6248110. [Google Scholar]
  22. Zhong, Sheng-hua; Liu, Yan; Liu, Yang. Bilinear Deep Learning for Image Classification // Proceedings of the 19th ACM International Conference on Multimedia. MM ’11. New York, NY, USA: ACM. 2011. p. 343–352. doi:10.1145/2072298.207234 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.