A Framework for Criminal Network Analysis Using Big Data

Md Ileas Pramanik; Wenping Zhang; Raymond Y. K. Lau; Chunping Li

doi:10.1109/ICEBE.2016.015

A Framework for Criminal Network Analysis Using Big Data

Md Ileas Pramanik, Wenping Zhang, Raymond Y. K. Lau, Chunping Li

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

12 Citations (Scopus)

Abstract

Big data analytics have been proposed as a disruptive technology that will reshape security and law enforcement agencies, which is a domain that relies on data analytics to achieve criminal network insights for better decision-making. Rooted in current literature, we review the landscape of criminal network analysis through the big data analytic framework in this paper. We identify the big data sources, big data platforms, tools, and applications related to criminal network analysis. We then present some real-world examples, research challenges, and future research directions of applying big data analytics to the criminal analytics domain.

Original language	English
Title of host publication	Proceedings - 13th IEEE International Conference on E-Business Engineering, ICEBE 2016 - Including 12th Workshop on Service-Oriented Applications, Integration and Collaboration, SOAIC 2016
Publisher	IEEE
Pages	17-23
ISBN (Print)	9781509061198
DOIs	https://doi.org/10.1109/ICEBE.2016.015
Publication status	Published - Nov 2016
Event	13th IEEE International Conference on E-Business Engineering, ICEBE 2016 - Macau, China Duration: 4 Nov 2016 → 6 Nov 2016

Conference

Conference	13th IEEE International Conference on E-Business Engineering, ICEBE 2016
Country/Territory	China
City	Macau
Period	4/11/16 → 6/11/16

Research Keywords

big data
criminal network
data mining
Social network analysis

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICEBE.2016.015

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@inproceedings{88fbb6ea66f64635b73bd5cc00f8ebf9,

title = "A Framework for Criminal Network Analysis Using Big Data",

abstract = "Big data analytics have been proposed as a disruptive technology that will reshape security and law enforcement agencies, which is a domain that relies on data analytics to achieve criminal network insights for better decision-making. Rooted in current literature, we review the landscape of criminal network analysis through the big data analytic framework in this paper. We identify the big data sources, big data platforms, tools, and applications related to criminal network analysis. We then present some real-world examples, research challenges, and future research directions of applying big data analytics to the criminal analytics domain.",

keywords = "big data, criminal network, data mining, Social network analysis",

author = "Pramanik, {Md Ileas} and Wenping Zhang and Lau, {Raymond Y. K.} and Chunping Li",

year = "2016",

month = nov,

doi = "10.1109/ICEBE.2016.015",

language = "English",

isbn = "9781509061198",

pages = "17--23",

booktitle = "Proceedings - 13th IEEE International Conference on E-Business Engineering, ICEBE 2016 - Including 12th Workshop on Service-Oriented Applications, Integration and Collaboration, SOAIC 2016",

publisher = "IEEE",

address = "United States",

note = "13th IEEE International Conference on E-Business Engineering, ICEBE 2016 ; Conference date: 04-11-2016 Through 06-11-2016",

}

Pramanik, MI, Zhang, W, Lau, RYK & Li, C 2016, A Framework for Criminal Network Analysis Using Big Data. in Proceedings - 13th IEEE International Conference on E-Business Engineering, ICEBE 2016 - Including 12th Workshop on Service-Oriented Applications, Integration and Collaboration, SOAIC 2016. IEEE, pp. 17-23, 13th IEEE International Conference on E-Business Engineering, ICEBE 2016, Macau, China, 4/11/16. https://doi.org/10.1109/ICEBE.2016.015

A Framework for Criminal Network Analysis Using Big Data. / Pramanik, Md Ileas; Zhang, Wenping; Lau, Raymond Y. K. et al.
Proceedings - 13th IEEE International Conference on E-Business Engineering, ICEBE 2016 - Including 12th Workshop on Service-Oriented Applications, Integration and Collaboration, SOAIC 2016. IEEE, 2016. p. 17-23.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A Framework for Criminal Network Analysis Using Big Data

AU - Pramanik, Md Ileas

AU - Zhang, Wenping

AU - Lau, Raymond Y. K.

AU - Li, Chunping

PY - 2016/11

Y1 - 2016/11

N2 - Big data analytics have been proposed as a disruptive technology that will reshape security and law enforcement agencies, which is a domain that relies on data analytics to achieve criminal network insights for better decision-making. Rooted in current literature, we review the landscape of criminal network analysis through the big data analytic framework in this paper. We identify the big data sources, big data platforms, tools, and applications related to criminal network analysis. We then present some real-world examples, research challenges, and future research directions of applying big data analytics to the criminal analytics domain.

AB - Big data analytics have been proposed as a disruptive technology that will reshape security and law enforcement agencies, which is a domain that relies on data analytics to achieve criminal network insights for better decision-making. Rooted in current literature, we review the landscape of criminal network analysis through the big data analytic framework in this paper. We identify the big data sources, big data platforms, tools, and applications related to criminal network analysis. We then present some real-world examples, research challenges, and future research directions of applying big data analytics to the criminal analytics domain.

KW - big data

KW - criminal network

KW - data mining

KW - Social network analysis

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85013040738&origin=recordpage

U2 - 10.1109/ICEBE.2016.015

DO - 10.1109/ICEBE.2016.015

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509061198

SP - 17

EP - 23

BT - Proceedings - 13th IEEE International Conference on E-Business Engineering, ICEBE 2016 - Including 12th Workshop on Service-Oriented Applications, Integration and Collaboration, SOAIC 2016

PB - IEEE

T2 - 13th IEEE International Conference on E-Business Engineering, ICEBE 2016

Y2 - 4 November 2016 through 6 November 2016

ER -

A Framework for Criminal Network Analysis Using Big Data

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Research Keywords

UN SDGs

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