A COMPARATIVE ANALYSIS OF CONVENTIONAL HADOOP WITH PROPOSED CLOUD ENABLED HADOOP FRAMEWORK FOR SPATIAL BIG DATA PROCESSING

• Main Authors: A. K. Tripathi, S. Agrawal, R. D. Gupta
• Format: Article
• Language: English
• Published: Copernicus Publications 2018-11-01
• Series: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-5/425/2018/isprs-annals-IV-5-425-2018.pdf

The emergence of new tools and technologies to gather the information generate the problem of processing spatial big data. The solution of this problem requires new research, techniques, innovation and development. Spatial big data is categorized by the five V’s: volume, velocity, veracity, variety and value. Hadoop is a most widely used framework which address these problems. But it requires high performance computing resources to store and process such huge data. The emergence of cloud computing has provided, on demand, elastic, scalable and payment based computing resources to users to develop their own computing environment. The main objective of this paper is to develop a cloud enabled hadoop framework which combines cloud technology and high computing resources with the conventional hadoop framework to support the spatial big data solutions. The paper also compares the conventional hadoop framework and proposed cloud enabled hadoop framework. It is observed that the propose cloud enabled hadoop framework is much efficient to spatial big data processing than the current available solutions.