TY - GEN
T1 - Hadoop based real-Time Big Data Architecture for remote sensing Earth Observatory System
AU - Rathore, M. Mazhar
AU - Ahmad, Awais
AU - Paul, Anand
AU - Daniel, Alfred
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/29
Y1 - 2016/1/29
N2 - Recently, Big Data analytics emerged as a hot topic because of the incredible growth of the information and communication technology. One of the exceedingly anticipated key contributors of the Big Data is real-Time Earth Observatory System (EOS). Although the data generated by the individual satellite in EOS may not be significant, the overall data generated across numerous satellites may yield to the significant amount of the Big Data. Thus, extracting the useful information in an efficient manner leads a system towards major computational challenges in EOS, such as, to analyze, to aggregate, and to store, where data is remotely collected. Therefore, the paper proposes a set of requirements for achieving pervasive, integrated information system of EOS and associated services (real-Time and offline data processing). The Big Data Architecture is also proposed to address all the aspect of the Big Data ecosystem and includes the following components: Data Acquisition Unit, Data Processing Unit, Data Storage Unit, and Data Analysis and Decision Unit. The proposed architecture is termed as Holistic as it considers the flow of data from satellites to services, which is designed for efficiently process and analyze the Big Data. Finally, a detailed analysis of remotely sensed earth observatory Big Data for Land and Sea area are provided using UBUNTU 14.04 LTS core™i5 machine with 3.2 GHz processor and 4 GB memory. The results show that the proposed network architecture efficiently process EOS data at a real-Time as well as offline.
AB - Recently, Big Data analytics emerged as a hot topic because of the incredible growth of the information and communication technology. One of the exceedingly anticipated key contributors of the Big Data is real-Time Earth Observatory System (EOS). Although the data generated by the individual satellite in EOS may not be significant, the overall data generated across numerous satellites may yield to the significant amount of the Big Data. Thus, extracting the useful information in an efficient manner leads a system towards major computational challenges in EOS, such as, to analyze, to aggregate, and to store, where data is remotely collected. Therefore, the paper proposes a set of requirements for achieving pervasive, integrated information system of EOS and associated services (real-Time and offline data processing). The Big Data Architecture is also proposed to address all the aspect of the Big Data ecosystem and includes the following components: Data Acquisition Unit, Data Processing Unit, Data Storage Unit, and Data Analysis and Decision Unit. The proposed architecture is termed as Holistic as it considers the flow of data from satellites to services, which is designed for efficiently process and analyze the Big Data. Finally, a detailed analysis of remotely sensed earth observatory Big Data for Land and Sea area are provided using UBUNTU 14.04 LTS core™i5 machine with 3.2 GHz processor and 4 GB memory. The results show that the proposed network architecture efficiently process EOS data at a real-Time as well as offline.
KW - Big Data
KW - Land and Sea Area
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=84964414116&partnerID=8YFLogxK
U2 - 10.1109/ICCCNT.2015.7395242
DO - 10.1109/ICCCNT.2015.7395242
M3 - Conference contribution
AN - SCOPUS:84964414116
T3 - 6th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2015
BT - 6th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2015
Y2 - 13 July 2015 through 15 July 2015
ER -
