Incorporating incident reports in bow-ties with Big Data techniques

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

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 proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Hazards 28 Conference Proceedings
Publisher	ImechE
Number of pages	9
Volume	2018-May
Edition	163
ISBN (Electronic)	9781911446637
Publication status	Published - 1 May 2018
Event	Hazards 28 Conference Proceedings: In Association with the Mary Kay O'Connor Process Safety Centre - Edinburgh International Conference Centre , Edinburgh, United Kingdom Duration: 15 May 2018 → 17 May 2018 Conference number: 28 https://icheme.myshopify.com/collections/conference-proceedings/products/hazards-28-conference-proceedings

Publication series

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

Conference

Conference	Hazards 28 Conference Proceedings
Abbreviated title	Hazards28
Country/Territory	United Kingdom
City	Edinburgh
Period	15/05/18 → 17/05/18
Internet address	https://icheme.myshopify.com/collections/conference-proceedings/products/hazards-28-conference-proceedings

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

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Hazards28 Manuscript v3Accepted author manuscript, 187 KBLicence: Unspecified

Cite this

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AB - 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.

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M3 - Conference contribution

VL - 2018-May

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Y2 - 15 May 2018 through 17 May 2018

ER -

Incorporating incident reports in bow-ties with Big Data techniques

Abstract

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Conference

UN SDGs

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