Loading

Hive Based Geospatial Analysis for Tracking and Envisioning of Geospatial Data in Hadoop Environment
Asha Kiran M1, M. Sreedevi2

1Asha Kiran M, M.Tech Scholar, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur (Andhra Pradesh), India.
2Dr. M. Sreedevi, Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur (Andhra Pradesh), India.
Manuscript received on 24 March 2019 | Revised Manuscript received on 05 April 2019 | Manuscript Published on 18 April 2019 | PP: 570-573 | Volume-7 Issue-6S March 2019 | Retrieval Number: F03110376S19/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Nowadays, the spatial data has gained prevalence as it has become an emerging subject in the technological world. It deals with the geographical location, boundaries, and features on the earth. To handle this spatial data, varied technologies and tools are available but are limited to some constraints. Different GIS tools are getting used to create and handle the spatial data for visualization. However, the outcomes have been unsatisfactory when it comes to handling huge data and analyzing the data by far. In this paper, as an improvement, a Hive-based spatial analysis has been proposed in Hadoop ecosystem to handle the spatial data, in fact, it can be called as spatial big data, as Hadoop can process a huge amount of data. Both GIS and Hadoop are integrated here to produce efficient outcomes, i.e.., in a pliable manner.
Keywords: GIS (Geographic Information System), Urbanization, Geospatial Data, Hadoop, Hive, ArcMap, ArcPy.
Scope of the Article: Smart Learning Methods and Environments