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Secured Geospatial Data Storage and Retrieval Using Spatial Hadoop Framework in Cloud Environment
S karthi,
Published in IEEE
Pages: 73 - 76
Due torecent advancement in satellite remote sensing technology, the volume of remote sensing image data grows exponentially, while the processing capability of existing computer system is hard to satisfy the requirements of remote sensing image data accessing. Technological advancements of Geospatial Information Systems (GIS) and cloud did the process in a simpler and acceptable way to share the fact and figures that makes complicated and time consuming decision making with more assurance level. Later on, increase in an IT reflected the storage, management, integration and correlation of huge data which has an impact in the efficiency of the operations growth rate. It is more important to secure the geospatial data which is stored in the public cloud to ensure who is accessing the data to reducerisks to information, national security, or perhaps for other reasons. This research proposes an efficient way of storing the geospatial data in public cloud using Spatial Hadoop mechanism in cloud computing environment. The Spatial Hadoop-GIS is a scalable and high performance spatial data warehousing system for running large scale spatial queries on Spatial Hadoop. Itwill support multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine. However, the associations are hesitant to store their touchy data on the cloud because of different protection and character following dangers. In the previous couple of years, a great deal of innovative work endeavors has been considered to list out the features and security for unified nodes for the development of makeup of data in cloud environment. At the outset, the model need to be refined with a similarity as the main concern. The article shows verification and approval protocol that pictorize the primary components of mysterious correspondence to be used for the cloud. © 2017 IEEE.
About the journal
JournalData powered by Typeset2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM)
PublisherData powered by TypesetIEEE
Open AccessNo