S.A. Krasnoperova
BIG DATA AS A DATABASE IN THE FIELD OF ENVIRONMENTAL RESEARCH
Annotation. This article discusses the possibilities of using technology for analyzing, systematizing and processing information using Big Data, as currently traditional methods are becoming ineffective. It has been revealed that this technology makes it possible to collect, store and analyze huge amounts of heterogeneous environmental information, for example: in the field of ecosystem monitoring studies; assessment of the impact of anthropogenic factors on the environment; forecasting natural phenomena. It is noted that the use of large amounts of data contributes to improving the efficiency of the technobiosphere management, improving the quality of monitoring and creating innovative methods for assessing the state of ecosystems. However, the successful implementation of these technologies requires the joint work of scientists, engineers and representatives of government agencies aimed at creating an infrastructure for data collection, storage and analysis, as well as training highly qualified specialists in the field of environmental analysis.
Keywords: big data, environmental research, information, Big Data structure.
For citation: Krasnoperova S.A. [Big data as a database in the field of environmental research] Upravlenie tekhnosferoi. 20245 vol. 8, issue 3. рр. 439–447. (In Russ.). DOI: 10.35634/UdSU.2025.88.31.006.
REFERENCES
1. Yusupov A.I. Rol' bol'shikh dannykh v biznese i nauke [The role of big data in business and science]. Nauka i obrazovanie – 2025: sbornik statei Mezhdunarodnoi nauchno-prakticheskoi konferentsii. [Science and education – 2025: collection of articles of the International Scientific and Practical Conference]. Petrozavodsk: Mezhdunarodnyi tsentr nauchnogo partnerstva "Novaya Nauka", 2025, pp. 158 – 162. (In Russ.).
2. Krasnoperova S. A. Ekologizatsiya obrazovatel'nogo protsessa v usloviyakh tsifrovoi sredy [Ecologization of the educational process in a digital environment]. Upravlenie tekhnosferoi. 2022, vol. 5, issue. 2, pp. 232 – 242. Available at: https://technosphere-ing.ru/ru/. (Accessed: 15.04.2025). (In Russ.).
3. P. R., E. S.V., Anilkumar Ch., Thilakaveni P., Moorthy U. Big data analytics and implementation challenges of machine learning in big data, Applied and Computational Engineering. 2023, vol. 2, no. 1, pp. 532 – 537.
4. Wei W. Looking at the demand of bank big data talents from the perspective of big data, Journal of Physics: Conference Series. 2020, vol. 1437, no. 1, pp. 012116.
5. Strategiya razvitiya informatsionnogo obshchestva v Rossiiskoi Federatsii na 2017 - 2030 gody: ukaz Prezidenta RF ot 9 maya 2017 g. № 203. 18 s. Available at: https://rtsis.rf/storage/uploads/2022/11/28/6384c39e5cd0e----9--2017--N-203-----.pdf. (Accessed: 15.04.2025).
6. Wang H. Sollege physical education and training in big data: a big data mining and analysis system, Journal of Healthcare Engineering. 2021, vol. 2021, p. 3585630.
7. Cuzzocrea A., Leung C.K., Olawoyin A.M., Fadda E. Supporting privacy-preserving big data analytics on temporal open big data, Procedia Computer Science. 2022, vol. 198, pp. 112 – 121.
8. Maggeramov Z.T., Abdullaev V.G., Maggeramova A.Z. Big Data: problemy, metody analiza, algoritmy // Informatsionnye tekhnologii. 2017. № 3. C. 42 – 52.
9. Big Data Analytics Market Study, Dresner Advisory Services, LLC, 2018, 94 p.
About the Author
Krasnoperova Svetlana Anatolyevna
Candidate of Biological Sciences, Associate Professor, Associate Professor of the Department of Oil and Gas Geology, M.S. Gutseriev Institute of Oil and Gas, Federal State Budgetary Educational Institution of Higher Education "Udmurt State University", 426034, Universitetskaya str., 1, Izhevsk, Russia.
E-mail: Адрес электронной почты защищен от спам-ботов. Для просмотра адреса в вашем браузере должен быть включен Javascript.
Full-text version - pdf(514kb)