TY - GEN
T1 - Big data analytics of geosocial media for planning and real-time decisions
AU - Rathore, M. Mazhar
AU - Paul, Anand
AU - Ahmad, Awais
AU - Imran, Muhammad
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/28
Y1 - 2017/7/28
N2 - Geosocial Network data can be served as an asset for the authorities to make real-time decisions and future planning by analyzing geosocial media posts. However, there are millions of Geosocial Network users who are producing overwhelming of data, called 'Big Data' that is challenging to be analyzed and make real-time decisions. Therefore, in this paper, we proposed an efficient system for exploring Geosocial Networks while harvesting data as well as user's location information. A system architecture is proposed that processes an abundant amount of various social networks' data to monitor Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning. The proposed system consists of five layers, i.e., data collection, data processing, application, communication, and data storage. The system deploys Spark at the top of the Hadoop ecosystem in order to run real-time analyses. Twitter and Flickr are analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, fires, Ebola virus, and snow. The system is evaluated with respect to efficiency while considering system throughput. We proved that the system has higher throughput and is capable of analyzing massive Geosocial Network data at real-time.
AB - Geosocial Network data can be served as an asset for the authorities to make real-time decisions and future planning by analyzing geosocial media posts. However, there are millions of Geosocial Network users who are producing overwhelming of data, called 'Big Data' that is challenging to be analyzed and make real-time decisions. Therefore, in this paper, we proposed an efficient system for exploring Geosocial Networks while harvesting data as well as user's location information. A system architecture is proposed that processes an abundant amount of various social networks' data to monitor Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning. The proposed system consists of five layers, i.e., data collection, data processing, application, communication, and data storage. The system deploys Spark at the top of the Hadoop ecosystem in order to run real-time analyses. Twitter and Flickr are analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, fires, Ebola virus, and snow. The system is evaluated with respect to efficiency while considering system throughput. We proved that the system has higher throughput and is capable of analyzing massive Geosocial Network data at real-time.
KW - Big Data
KW - Geosocial Network
KW - Hadoop
KW - Spark
UR - http://www.scopus.com/inward/record.url?scp=85028334049&partnerID=8YFLogxK
U2 - 10.1109/ICC.2017.7996545
DO - 10.1109/ICC.2017.7996545
M3 - Conference contribution
AN - SCOPUS:85028334049
T3 - IEEE International Conference on Communications
BT - 2017 IEEE International Conference on Communications, ICC 2017
A2 - Debbah, Merouane
A2 - Gesbert, David
A2 - Mellouk, Abdelhamid
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Communications, ICC 2017
Y2 - 21 May 2017 through 25 May 2017
ER -