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DEVELOPMENT OF HIERARCHICAL DISTRIBUTED GIS SYSTEM
Abstract
Modern scientific research centers require the fast, fault-tolerant storing systems for the collecting and processing of large amounts of scientific data originating from different sources and serving a great variety of client applications all over the world. This problem has great importance in many fields of science, such as geoinformatics, climate modeling, high-energy physics, astrophysics, and others. It is reasonable to use the concept of distributed computing to preprocess, normalize and aggregate scientific data at the level of Edge computing nodes and to provide the GRID or cloud computing models on the higher level to perform complicated analysis of data aggregations in the central computer cluster. The scientific problems discussed in this paper are the following: Development and research of reversible transformation method for multidimensional GIS data into a hierarchical multi-scale structure with preservation of all information characteristics. In our previous theoretical research on this subject, we have shown that the hierarchical representation of data allows reducing the number of I/O operations needed to perform various kinds of user's requests and allows reducing the memory and computational resources consumption; Research on the data storage and processing system model (SDDS) which implements a hierarchical representation of data at the hardware and software level. We have to adapt the hardware and file systems to a new format and logical structure of the data being stored and processed at the software site; Research on hierarchical indexing methods of source data, allowing to perform fast search queries.
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M. Folk, G. Heber, Q. Koziol, E. Pourmal, and D. Robison, An overview of the HDF5 technology suite and its applications, in Proceedings of the EDBT/ICDT 2011 Workshop on Array Databases, USA, 2011, pp. 36-47;
W. Litwin, R. Moussa, Th. J. E. Schwarz., LH*RS: a highly available distributed data storage. [Online] Available: http://portal.acm.org/citation.cfm?id=1316816
W. Litwin, H. Yakouben, Th. Schwarz, LH* RS: A Scalable Distributed Data Structure for P2P Environment ACM-TODS, Sept 2005.;
A. Kokoulin, Methods for large image distributed processing and storage. EUROCON 2013, Horvatia, pp.1606-1610. DOI: 10.1109/EUROCON.2013.6625191;
Y. Geng, X. Huang, G. Yang, SwiftArray: Accelerating Queries on Multidimensional Arrays, Tsinghua Science and Technology, ISSN 1007 0214 12/13 pp 521-530 Volume 19, Number 5, October 2014
K. Wu, S. Ahern, E. W. Bethel, J. Chen, H. Childs, E. Cormier-Michel, C. Geddes, J. Gu, H. Hagen, B. Hamann, W. Koegler, J. Lauret, J. Meredith, P. Messmer, E. Otoo, V. Perevoztchikov, FastBit: Interactively searching massive data, J. Phys.: Conf. Ser., vol. 180, no. 1, 012053, Jul. 2009.
D. Stanfill, Using Image Pyramids for the Visualization of Large Terrain Data Sets, International Journal of Imaging Systems and Technology, vol. 3, 1991.
H. Kim., A parallel algorithm for the biorthogonal wavelet transform without multiplication. CIS'04 Proceedings of the First international conference on Computational and Information Science pp. 207-212, Springer-Verlag Berlin, Heidelberg, 2004.
Suhad Faisal Behadili. Adaptative modeling of urban dynamics with mobile phone database. Network-ing and Internet Architecture [cs.NI]. Université du Havre, 2016.
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