Incorporating incident reports in bow-ties with Big Data techniques

Coen Van Gulijk, Miguel Figueres Esteban, Peter Hughes, Paul McCullogh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper shows how text analysis techniques are used to automatically select text-based incident reports to import in commercial BowTie software. Text is analysed with the TFIDF method to create an ontology that facilitates the creation of search queries to match individual reports to threats in a BowTie. The method is illustrated with an example from the railway industry but it is equally applicable in the chemical industry. The approach saves time and allows for the analysis of huge volumes of incident reports. This work demonstrates big-data techniques can add value to chemical safety and paves the way to digital safety management systems.
Original languageEnglish
Title of host publicationHazards 28 Conference Proceedings
PublisherImechE
Number of pages9
Volume2018-May
Edition163
ISBN (Electronic)9781911446637
Publication statusPublished - 1 May 2018
EventHazards 28 Conference Proceedings: In Association with the Mary Kay O'Connor Process Safety Centre - Edinburgh International Conference Centre , Edinburgh, United Kingdom
Duration: 15 May 201817 May 2018
Conference number: 28
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Publication series

NameInstitution of Chemical Engineers Symposium Series
ISSN (Print)0307-0492

Conference

ConferenceHazards 28 Conference Proceedings
Abbreviated titleHazards28
CountryUnited Kingdom
CityEdinburgh
Period15/05/1817/05/18
Internet address

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  • Cite this

    Van Gulijk, C., Figueres Esteban, M., Hughes, P., & McCullogh, P. (2018). Incorporating incident reports in bow-ties with Big Data techniques. In Hazards 28 Conference Proceedings (163 ed., Vol. 2018-May). (Institution of Chemical Engineers Symposium Series). ImechE.