TY - GEN
T1 - Topology reduction and probabilistic information extraction for large data-sets
T2 - 2nd International Conference on Information and Communication Technologies for Disaster Management, ICT-DM 2015
AU - Trovati, Marcello
AU - Asimakopoulou, Eleana
AU - Bessis, Nik
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/8
Y1 - 2016/2/8
N2 - The dynamical and probabilistic properties of the relationships among data modelled by real-world networks have drawn extensive research from a several interdisciplinary fields. They, in fact, can successfully identify the main properties of large data-sets. However, a deep analysis of such networks is likely to generate information of little use due to their inherent complexity, as well as the inconsistencies of data modelled by them. In this paper, we discuss the evaluation of a method as part of ongoing research which aims to extract, assess and identify relevant information based on the mutual probabilistic relationships among the data captured by Big Data. In order to validate and support our approach, a large dataset capturing information on the seismological activity provided by the European-Mediterranean Seismological Centre is considered. We will show that this approach provides a scalable, accurate and useful tool to enhance the state of the art research within disaster management. The approach discussed in this paper further supports our effort to create a big data analytics tool aiming to extract actionable intelligence from a variety of big datasets.
AB - The dynamical and probabilistic properties of the relationships among data modelled by real-world networks have drawn extensive research from a several interdisciplinary fields. They, in fact, can successfully identify the main properties of large data-sets. However, a deep analysis of such networks is likely to generate information of little use due to their inherent complexity, as well as the inconsistencies of data modelled by them. In this paper, we discuss the evaluation of a method as part of ongoing research which aims to extract, assess and identify relevant information based on the mutual probabilistic relationships among the data captured by Big Data. In order to validate and support our approach, a large dataset capturing information on the seismological activity provided by the European-Mediterranean Seismological Centre is considered. We will show that this approach provides a scalable, accurate and useful tool to enhance the state of the art research within disaster management. The approach discussed in this paper further supports our effort to create a big data analytics tool aiming to extract actionable intelligence from a variety of big datasets.
KW - Data analytics
KW - Disaster Management
KW - Information extraction
KW - Knowledge discovery
KW - Networks
KW - Seismological data
UR - http://www.scopus.com/inward/record.url?scp=84964203224&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964203224&partnerID=8YFLogxK
U2 - 10.1109/ICT-DM.2015.7402027
DO - 10.1109/ICT-DM.2015.7402027
M3 - Conference proceeding (ISBN)
AN - SCOPUS:84964203224
SN - 9781479999231
T3 - Proceedings of the 2015 2nd International Conference on Information and Communication Technologies for Disaster Management, ICT-DM 2015
SP - 116
EP - 121
BT - Proceedings of the 2015 2nd International Conference on Information and Communication Technologies for Disaster Management, ICT-DM 2015
A2 - Hadjadj-Aoul, Yassine
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 November 2015 through 2 December 2015
ER -