Building database of WEBGIS for the exchange of marine data between Vietnam and ASEAN countries
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DOI:
https://doi.org/10.15625/1859-3097/19/3B/14513Keywords:
WebGIS, ASEAN, marine data.Abstract
The system of oceanic database management and exchange is built with the purpose of exchanging the oceanic data between Vietnam and other ASEAN countries. The system can meet the demand of sharing and exchanging oceanic data through internet connection between Vietnam and ASEAN member countries. Besides, the system of oceanic database management and exchange can meet the demands of researches, managements and share of data domestically and internationally. In this paper, we focused on the details of the system of oceanic database management and exchange, such as hardware and software, data storage, data format and data structure, data management and integration, and other issues of interface, security, standards. The WEBGIS oceanic thematic database is properly built and managed for exchanging data domestically and with other ASEAN countries.
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Nguyễn Đình Kỳ, 2016. Xây dựng CSDL GIS sở dữ liệu và Atlas điện tử tổng hợp vùng Tây Nguyên.
World Data Center, Available at: https://en.wik-ipedia.org/wiki/World_Data _Center. (access 20 June 2019).
World Ocean Database, Available at: http://wod.iode.org (access 22 June 2019).
National Center Invironmental Information, Available at: https://www.nodc.noaa.gov/ (access 25 July 2019).
Khan, M., Wu, X., Xu, X., and Dou, W., 2017. Big data challenges and opportunities in the hype of Industry 4.0. In 2017 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.
Vũ Văn Tác, Ngô Mạnh Tiến, 2012. Dữ liệu hải dương học Biển Đông trong cơ sở dữ liệu biển thế giới 2009. Tuyển tập nghiên cứu biển, Tập XVIII. Tr. 35–45.
Aji, A., Sun, X., Vo, H., Liu, Q., Lee, R., Zhang, X., ... and Wang, F., 2013. Demonstration of Hadoop-GIS: a spatial data warehousing system over MapReduce. In Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 528–531). ACM.
Eldawy, A., and Mokbel, M. F., 2015. Spatialhadoop: A mapreduce framework for spatial data. In 2015 IEEE 31st international conference on Data Engineering (p. 1352–1363). IEEE.
Yang, C., Yu, M., Hu, F., Jiang, Y., and Li, Y., 2017. Utilizing cloud computing to address big geospatial data challenges. Computers, Environment and Urban Systems, 61, 120–128.
Hershey, P. C., and Silio, C. B., 2012. Procedure for detection of and response to distributed denial of service cyber attacks on complex enterprise systems. In 2012 IEEE International Systems Conference SysCon 2012 (pp. 1–6). IEEE.
High Efficiency, Firewalls and ArcGIS Server, Available at:http://enterprisearcgis.com/en/ server/latest/administer/linux/firewall-and-arcgis-server.html. (access 20 July 2019).
Jin, S., Wang, X., Luan, C., Zhang, H., and Guo, Y., 2012. Construction of Marine Oil Spill Response Information System Based on ArcGIS Server. In Recent Advances in Computer Science and Information Engineering (pp. 575–581). Springer, Berlin, Heidelberg.
Yang, C., Huang, Q., Li, Z., Liu, K., and Hu, F., 2017. Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53.
Grecea, C., Herban, S., and Vilceanu, C. B., 2016. WebGIS solution for urban planning strategies. Procedia engineering, 161, 1625–1630.
Wang, F., Aji, A., Liu, Q., and Saltz, J., 2011. Hadoop-GIS: A high performance spatial query system for analytical medical imaging with MapReduce. Center for Comprehensive Informatics, Technical Report. Available at: http://www3. cs. stonybrook. edu/~ fuswang/papers/CCI-TR-2011-3. pdf (access 21 September 2015).
Fustes, D., Cantorna, D., Dafonte, C., Iglesias, A., and Arcay, B., 2012. Applications of cloud computing and gis for ocean monitoring through remote sensing. In Smart Sensing Technology for Agriculture and Environmental Monitoring (pp. 303–321). Springer, Berlin, Heidelberg.
Vo, H., Aji, A., and Wang, F., 2014. SATO: a spatial data partitioning framework for scalable query processing. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 545–548). ACM.