DocumentCode :
116438
Title :
MOSAIC: Criminal network analysis for multi-modal surveillance and decision support
Author :
Seidler, Patrick ; Adderley, Richard ; Badii, Anush ; Raffaelli, Matteo
Author_Institution :
A.E. Solutions (BI) Ltd., Badsey, UK
fYear :
2014
fDate :
17-20 Aug. 2014
Firstpage :
257
Lastpage :
260
Abstract :
With increasing complexity of the social systems under surveillance, demand grows for automated tools which are able to support end users in making sense of situational context from the amount of available data and incoming data streams. This paper presents MOSAIC (Multi-Modal Situation Assessment and Analytics Platform), a semantically integrated system which aims at exploiting multi-modal data analysis comprising advanced tools for text and data mining, criminal network analysis, and decision support. The aim is to provide, from an enriched context, an understanding of behaviour of the system under surveillance thus supporting authorities in their decision making processes. Specific measures and algorithms have been developed to support analysts in retrieving, analysing, and disrupting criminal networks, identifying offenders that pose the greatest harm aligned with domain-specific strategies, as well as enabling the investigation of intervention strategies. A case study is provided in order to illustrate the system in practice.
Keywords :
data analysis; data mining; decision support systems; social sciences computing; text analysis; MOSAIC; criminal network analysis; criminal network disruption; criminal network retrieval; data mining; decision making processes; decision support; domain-specific strategies; multimodal data analysis; multimodal situation assessment-and-analytics platform; multimodal surveillance; social system complexity; system behaviour; text mining; Context; Data models; Engines; Semantics; Social network services; Text mining; Data mining; entity recognition and resolution; intelligence analysis; semantic interoperability; social and criminal network analysis; text mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/ASONAM.2014.6921593
Filename :
6921593
Link To Document :
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