Analytic provenance for criminal intelligence analysis

Junayed Islam; Kai Xu; William Wong

doi:10.11959/j.issn.2096-109x.2018016

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Analytic provenance for criminal intelligence analysis

安全数据可视化 | 更新时间：2024-06-03

- Analytic provenance for criminal intelligence analysis
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- 网络与信息安全学报 2018年4卷第2期页码：18-33
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- 作者简介：
  
  [ "Junayed Islam is a PhD student at Middlesex University London. His research interests include big data,visualization and visual analytics. Main focus of his current work is to design and develop UI for reconstructing past criminal situation by combining geospatial and temporal visualization techniques,network visualization tools,with argument and narrative structuring techniques to formulate plausible explanations in criminal intelligence." ]
  [ "Kai Xu is an Associate Professor in Data Analytics at Middlesex University London. He has over 15 year experience in data visualization and analytics research in both the academic and industry context. He has extensive experience working with the UK government departments and leading defence companies in data analytics projects. His work has won a few international data visualization awards. More details are here:https://kaixu.me/." ]
  [ "William Wong is Professor of HumanComputer Interaction and Head,Interaction Design Centre at Middlesex University London. His research interest is in the representation design of information and the interaction with user interfaces that support decision making in complex dynamic environments. He uses a cognitive engineering approach to understand the nature of expertise and to model the nature of cognitive work in order to design better systems. He is currently investigating the problems of visual analytics in sense making domains such as intelligence analysis,financial systemic risk analysis,and low literacy users. He has received over USD $25 mil in research grants and together with his students and colleagues have published over 100 refereed articles." ]
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2018016
  中图分类号：
- 网络首发：2018-02，
  
  纸质出版：2018-02-15
- 稿件说明：
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Junayed Islam, Kai Xu, William Wong. Analytic provenance for criminal intelligence analysis[J]. 网络与信息安全学报, 2018,4(2):18-33.
Junayed Islam, Kai Xu, William Wong. Analytic provenance for criminal intelligence analysis[J]. 网络与信息安全学报, 2018,4(2):18-33. DOI： 10.11959/j.issn.2096-109x.2018016.

DOI：

摘要

In criminal intelligence domain where solution discovery is often serendipitous

it demands techniques to provide transparent evidences of top-down and bottom-up analytical processes of analysts while sifting through or transforming sourced data to provide plausible explanation of the fact.Management and tracing of such security sensitive analytical information flow originated from tightly coupled visualizations into visual analytic system for criminal intelligence that triggers huge amount of analytical information on a single click

involves design and development challenges.In this research paper

we have introduced a system called “PROV” to capture

visualize and utilize analytical information named as analytic provenance by considering such challenges.A video demonstrating its features is available online at https://streamable.com/r8mlx.Prior to develop this system for criminal intelligence analysis

we conducted a systematic research to outline the requirements and technical challenges.We gathered such information from real police intelligence analysts through multiple sessions who are the end users of a large heterogeneous event-driven modular Analyst’s User Interface (AUI) of the project VALCRI (Visual Analytics for Sensemaking in Criminal Intelligence)

developed by using visual analytic technique.We have proposed a semantic analytic state composition technique to trigger new insight by schematizing captured reasoning states.To evaluate the system we carried out few subjective feedback sessions with the end-users of the project and found very positive feedback.We also have tested our event triggered analytic state capturing protocol with an external geospatial and temporal crime analysis system and found that our proposed technique works generically for both small and large complex visual analytic systems.

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references

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