TY - GEN
T1 - Towards a Multidimensional Model for Terrorist Attacks Analysis and Mining
AU - Saidi, Firas
AU - Trabelsi, Zouheir
AU - Ghezala, Henda Ben
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/30
Y1 - 2018/10/30
N2 - Terrorist attacks are in exponentially growth and cause daily dire human and material damages. Analyzing potential terrorism data is without doubt an indispensable task not only to understand these terrorist events, mainly terrorist implicated actors, their communities and their operation methods and tactics, but also to predict future attacks. However, terrorist events are commonly conducted differently and rarely fit the models of conventional attacks. Thus, terrorist events are usually hard to analyze, mine and investigate. In this paper, we propose a multidimensional model, named Terrorist Data Warehouse (TerDW), for allowing terrorist investigators to perform interesting and specific analysis for decision-making objectives. Practically, while consulting gathered information relative to specific terrorist attacks, such as attacks' locations, dates, used weapons, and implicated attackers, investigators can query the proposed data warehouse (TerDW) using multidimensional queries. The proposed model is based on consistent dimensions and measures. Terrorist attacks data are extracted from Global Terrorism Database (GTD), a well-known database that includes information about terrorist events that took place from 1970 to 2017. Examples of queries have been conducted to evaluate the efficiency of the proposed model. The queries' results demonstrate clearly that the proposed model allows investigators to carry out more appropriate multidimensional analysis, investigations and decisions.
AB - Terrorist attacks are in exponentially growth and cause daily dire human and material damages. Analyzing potential terrorism data is without doubt an indispensable task not only to understand these terrorist events, mainly terrorist implicated actors, their communities and their operation methods and tactics, but also to predict future attacks. However, terrorist events are commonly conducted differently and rarely fit the models of conventional attacks. Thus, terrorist events are usually hard to analyze, mine and investigate. In this paper, we propose a multidimensional model, named Terrorist Data Warehouse (TerDW), for allowing terrorist investigators to perform interesting and specific analysis for decision-making objectives. Practically, while consulting gathered information relative to specific terrorist attacks, such as attacks' locations, dates, used weapons, and implicated attackers, investigators can query the proposed data warehouse (TerDW) using multidimensional queries. The proposed model is based on consistent dimensions and measures. Terrorist attacks data are extracted from Global Terrorism Database (GTD), a well-known database that includes information about terrorist events that took place from 1970 to 2017. Examples of queries have been conducted to evaluate the efficiency of the proposed model. The queries' results demonstrate clearly that the proposed model allows investigators to carry out more appropriate multidimensional analysis, investigations and decisions.
KW - GTD
KW - MDX
KW - OLAP analysis
KW - TerDW
KW - Terrorist Attack
UR - http://www.scopus.com/inward/record.url?scp=85114864589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114864589&partnerID=8YFLogxK
U2 - 10.1109/ICCTA45985.2018.9499167
DO - 10.1109/ICCTA45985.2018.9499167
M3 - Conference contribution
AN - SCOPUS:85114864589
T3 - 28th International Conference on Computer Theory and Applications, ICCTA 2018 - Proceedings
SP - 55
EP - 59
BT - 28th International Conference on Computer Theory and Applications, ICCTA 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th International Conference on Computer Theory and Applications, ICCTA 2018
Y2 - 30 October 2018 through 1 November 2018
ER -