1 Maiorov A.A.

Moscow State University of Geodesy and Cartography (MIIGAiK)

The author examines the current state of development and processing large spatio-temporal data. Parallel computing is seen as a new technology for handling big volumes of it. The use of ultra-high-speed processing as an analysis and decision-making tool in the earth sciences is due to three reasons: the expansion of measuring systems’ capabilities; improvement of spatial models and methods for solving complex spatial tasks; increase in the productivity of computing systems. The concept of “computational geoscience” is introduced; it is characterized by large-scale nonlinear models that contain large amounts of data and a wide variety of its types. It is proved that the voluminous data problem has existed in the earth sciences for over half a century, and ultra-fast processing aims to solving it. The main motivations for the application of parallel programming in earth sciences are revealed. The features of large spatio-temporal data, as the basis for processing and storage in geoinformatics, are described.
The research was carried out within the state assignment of The Ministry of Science and Higher Education of the Russian Federation (No. 0708-2020-0001).
1.   Andreeva O. A., Tsvetkov V. Ya., Oznamets V. V. Geoinformatsionnoe massovoe modelirovanie. Informatsiya i kosmos, 2020, no. 2, pp. 106–112.
2.   Bakhareva N. A. Prostranstvennye otnosheniya kak faktor otsenki zemel'. ITNOU: Informatsionnye tekhnologii v nauke, obrazovanii i upravlenii, 2018, no. 6 (10), pp. 61–69.
3.   Buravtsev A. V., Tsvetkov V. Ya. Oblachnye vychisleniya dlya bol'shikh geoprostranstvennykh dannykh. Informatsiya i kosmos, 2019, no. 3, pp. 110–115.
4.   Kosmicheskie issledovaniya zemnykh resursov: Metody i sredstva izmerenii i obrabotki informatsii (Materialy shkoly-seminara). Moskva: Nauka, 1976, 384 p.
5.   Kudzh S. A., Tsvetkov V. Ya. Geoinformatika: Monografiya. Moskva: MAKS Press, 2019, 224 p.
6.   Majorov A. A., Materuhin A. V. Analiz sushchestvujushchih tehnologij obrabotki potokov prostranstvenno-vremennyh dannyh dlya sovremennyh informatsionno-izmeritel'nyh sistem. Izmeritel'naya tehnika, 2017, no. 4, pp. 31–34.
7.   Maiorov A. A., Materukhin A. V., Kondaurov I. N. Struktura sistemy obrabotki potokovykh dannykh v geosensornykh setyakh. Izv. vuzov «Geodeziya i aerofotos"emka», 2018, Vol. 62, no. 6, pp. 712–719. DOI: 10.30533/0536-101X-2018-62-6-712-719.
8.   Maiorov A. A., Materukhin A. V. Kontseptual'naya model' informatsionno-izmeritel'noi sistemy na baze raspredelennykh setei intellektual'nykh geosensorov. Izmeritel'naya tekhnika, 2018, no. 5, pp. 26–31.
9.   Matchin V. T. Sostoyanie i razvitie infrastruktury prostranstvennykh dannykh. Obrazovatel'nye resursy i tekhnologii, 2015, no. 1 (9), pp. 137–144.
10.   Savinykh V. P. Informatsionnye prostranstvennye otnosheniya. Obrazovatel'nye resursy i tekhnologii, 2017, no. 1 (18), pp. 79–88. DOI: 10.21777/2312-5500-2017-1-79-88.
11.   Asaduzzaman A., Gupta D. (2020) Geospatial Cyberinfrastructure for regional economic Growth. 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). - IEEE. pp. 723-726.
12.   Fischer J. R. et al. (1991) Applications of the massively parallel machine, the MasPar MP-1, to Earth sciences. Earth and Atmospheric Remote Sensing. - International Society for Optics and Photonics, Volume 1492, pp. 229-238.
13.   Hailong Z., Hongxia L. (2019) Urban fine management based on multi-source spatial data fusion. Bulletin of Surveying and Mapping, no. 12, pp. 108.
14.   Mahdiyar A. et al. (2019) A prototype decision support system for green roof type selection: A cybernetic fuzzy ANP method. Sustainable Cities and Society, Vol. 45, pp. 101532.
15.   (2019) Members B. I. G. D. C. Database resources of the BIG Data Center in 2019. Nucleic Acids Research, Vol. 47, no. Database issue, D8 p.
16.   Morley C. V. et al. (2017) Forward and inverse modeling of the emission and transmission spectrum of GJ 436b: investigating metal enrichment, tidal heating, and clouds. The Astronomical Journal, Volume 153, no. 2, pp. 86.
17.   Quénol H. et al. (2017) Which climatic modeling to assess climate change impacts on vineyards? .
18.   Wang X., Feng L., Zhao H. (2019) Fast image encryption algorithm based on parallel computing system. Information Sciences, Vol. 486, pp. 340-358.
19.   Wen X. et al. (2020) Point2SpatialCapsule: Aggregating features and spatial relationships of local regions on point clouds using spatial-aware capsules. IEEE Transactions on Image Processing, Vol. 29, pp. 8855-8869.
20.   Wu Q. et al. (2020) Parallel computing in railway research. International Journal of Rail Transportation, Vol. 8, no. 2, pp. 111-134.
21.   Zuo R., Xiong Y. (2020) Geodata science and geochemical mapping. Journal of Geochemical Exploration, Vol. 209, pp. 106431.
Maiorov A.A., 
(2020) On the issues of ultra-fast processing voluminous spatio-temporal data. Geodesy and cartography = Geodezia i Kartografia, 81(12), pp. 50-56. (In Russian). DOI: 10.22389/0016-7126-2020-966-12-50-56
Publication History
Received: 08.09.2020
Accepted: 10.12.2020
Published: 31.12.2020


2020 December DOI:

QR-code page

QR-код страницы