Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: http://www.sciepub.com/journal/jcsa Editor-in-chief: Minhua Ma, Patricia Goncalves
Open Access
Journal Browser
Go
Journal of Computer Sciences and Applications. 2020, 8(2), 40-45
DOI: 10.12691/jcsa-8-2-1
Open AccessArticle

Associations Rankings Model for Cellular Surveillance Analysis

Michael M Kangethe1, and Robert Oboko1

1School of Computing and Informatics, University of Nairobi, Nairobi, Kenya

Pub. Date: July 22, 2020

Cite this paper:
Michael M Kangethe and Robert Oboko. Associations Rankings Model for Cellular Surveillance Analysis. Journal of Computer Sciences and Applications. 2020; 8(2):40-45. doi: 10.12691/jcsa-8-2-1

Abstract

This is the study and implementation of an association surveillance technology framework model for GSM mobile networks. This enables the efficient and automated identification of entity associations and potential relationships between several entities and events based on a hierarchy of interactions. The approach to this problem is to develop a weighted graph network G=(V(W),E) where V={w(SID1),w(SID2),…,w(SIDn)} w represents the association sore between the ShadowID represented as a node SID and the Person of interest(POI) represented as the root node. This model and algorithm are developed as an automated surveillance system framework that enables the tracking of individual entities ' relationships with others based on their interaction and by their physical proximity to the entity of interest. As the future of automated surveillance will not just include the collection of geographic and visual data but also intelligence on the particular entity's interaction log information from activity patterns which can be mapped in an easy to present format to the interested parties.

Keywords:
graphs surveillances data mining association scoring GUI: - Graphical User Interface national security predictive analytics Shadow ID POI MS (Mobile Station)

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  Bruno Agard, Catherine Morency, Martin Trépanier. 2007. Mining Public transport user behavior from smart card data.
 
[2]  Vikas Grover, Richard Adderley, Max Bramer,. Review of Current Crime Prediction Techniques. 2009.
 
[3]  Nassar, Khaled, Data-Mining of State Transportation Agencies Projects Databases. 12. 2007.
 
[4]  Tom M. Mitchell 1997. Machine Learning.
 
[5]  Van der Veer, H.T. Roos, A. van der Zanden, Data mining for intelligence led policing. 2009.
 
[6]  Dale Dzemydiene, Raimundas Vaitkevicius, Ignas Dzemyda, Pattern recognition based on statistics and structural equation models in multi-dimensional data warehouses of social behavioral data, pg 4 -10. 2010.
 
[7]  Han J, Kamber, Data mining: concepts and techniques. 2006.
 
[8]  Thuraisingham, Bhavani, Data Mining, National Security, Privacy and Civil Liberties. SIGKDD Explorations. 4. 1-5. (2002).
 
[9]  Lee BS, Snapp RR, Musick R, Critchlow T, Metadata models for ad hoc queries on terabyte-scale scientific simulations. 2002
 
[10]  Najera, J., 2020. Graph Theory — History & Overview. [online] Medium. Available at: <https://towardsdatascience.com/graph- theory-history-overview-f89a3efc0478> [Accessed 15 May 2020].
 
[11]  Singh, L., 2020. Exploring Graph Mining Approaches for Dynamic Heterogeneous Networks. Georgetown University.
 
[12]  McCue, C, Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis: Second Edition. Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis: Second Edition. 1-393, (2015).