Towards Real-Time Probabilistic Risk Assessment by Sensing Disruptive Events from Streamed News Feeds

Author

Burnap, Pete ; Rana, Omer ; Pauran, Nargis ; Bowen, Phil

Author_Institution

Sch. of Comput. Sci. & Inf., Cardiff Univ., Cardiff, UK

fYear

2014

fDate

2-4 July 2014

Firstpage

608

Lastpage

613

Abstract

Risk management has become an important concern over recent years and understanding how risk models could be developed based on the availability of real time (streaming) data has become a challenge. As the volume and velocity of event data (from news media, for instance) continues to grow, we investigate how such data can be used to inform the development of dynamic risk models. A Bayesian Belief Network based approach is adopted in this work, which is able to make use of priors derived from a variety of different news sources (based on data available in RSS feeds).

Keywords

Bayes methods; belief networks; data handling; electronic publishing; risk management; Bayesian belief network based approach; RSS feeds; disruptive event sensing; dynamic risk model; event data velocity; event data volume; news media; real time streaming data; real-time probabilistic risk assessment; risk management; streamed news feeds; Bayes methods; Computational modeling; Data models; Feeds; Media; Meteorology; Roads; dynamic data based modelling; risk modelling; streaming data and event analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Complex, Intelligent and Software Intensive Systems (CISIS), 2014 Eighth International Conference on

Conference_Location

Birmingham

Print_ISBN

978-1-4799-4326-5

Type

conf

DOI

10.1109/CISIS.2014.87

Filename

6915582