Cloud Service for Multidimensional Processing of Quantitative Data for Solving Geological
https://doi.org/10.25205/1818-7900-2021-19-3-40-49
Abstract
The study describes the developed cloud web service for multidimensional processing of quantitative data for solving a wide class of scientific geological tasks. The computing node “Multidimensional methods of data analysis” provides processing of tabular data using various methods of modern data analysis and allows to set their parameters and visualize the results. The node includes wide range of methods such as data preprocessing, descriptive statistics, cluster analysis, factor analysis, correlation analysis, regression analysis. Computing node “Multidimensional methods of data analysis” is a part of Computational analytical geological environment of State Geological Museum of RAS and is integrated with its services. At the same time, the computing node is an independent cloud web service which implements REST API for interaction with it. This allows a wide range of users to access multidimensional data analysis methods located on a computing node and provides capabilities of its integration into information systems as a thirdparty application for processing tabular data.
About the Authors
A. A. Zagumennov
Institute of Automation and Control Processes of the Far East Branch of the Russian Academy of Sciences
Russian Federation
Russian Federation
Aleksei A. Zagumennov, Researcher
Researcher ID R-4407-2016
Vladivostok
V. V. Naumova
V. I. Vernadsky State Geological Museum of the Russian Academy of Sciences
Russian Federation
Russian Federation
Vera V. Naumova, Doctor of Sciences (Geology and Mineralogy)
Researcher ID J-9039-2018
Moscow
V. S. Eremenko
V. I. Vernadsky State Geological Museum of the Russian Academy of Sciences
Russian Federation
Russian Federation
Vitaliy S. Eremenko, Junior Researcher
Researcher ID Q-3678-2016
Moscow
References
1. Naumova V. V., Platonov K. A., Eremenko V. S., Patuk M. I., Dyakov S. E. Information and analytical environment for supporting scientific research in geology: current state and development prospects. In: Proceedings of the XVII International conference “Distributed information and computing resources. (DICR-2019)”. Novosibirsk, 2019, pp. 139–147. (in Russ.) DOI 10.25743/ICT.2019.70.61.021
2. Eremenko V. S., Naumova V. V., Platonov K. A., Dyakov S. E., Eremenko A. S. The main components of a distributed computational and analytical environment for the scientific study of geological systems. Russian Journal of Earth Sciences, 2018, vol. 18, iss. 6. DOI 10.2205/2018ES000636
3. Eremenko V. S., Naumova V. V., Zagumennov A. A., Bulov S. V. Cloud technologies for development of geographically distributed computational and analytical Geological environment. Computational Technologies, 2021, vol. 26, no. 1, pp. 86–98. (in Russ.)
4. Ivanov S. D. Interactive Web Application Based Geosensors Registry. Computer research and modeling, 2016, vol. 8, no. 4, pp. 621–632. (in Russ.)
5. Tkaczyk D., Szostek P., Fedoryszak M., Dendek P., Bolikowski L. CERMINE: automatic extraction of structured metadata from scientific literature. International Journal on Document Analysis and Recognition, 2015, vol. 18, no. 4, pp. 317–335.
6. Maynard D., Bontcheva K., Augenstein I. Natural Language Processing for the Semantic Web. Synthesis Lectures on the Semantic Web: Theory and Technology, 2016, vol. 6, no. 2, pp. 1–194.
7. Moser L., Thuraisingham B., Zhang J. Services in the cloud. IEEE transactions on services computing, 2015, vol. 8, no. 2, pp. 172–174.
Review
For citations:
Zagumennov A.A., Naumova V.V., Eremenko V.S. Cloud Service for Multidimensional Processing of Quantitative Data for Solving Geological. Vestnik NSU. Series: Information Technologies. 2021;19(3):40-49. (In Russ.) https://doi.org/10.25205/1818-7900-2021-19-3-40-49
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