1. A systematic approach for discovering causal dependencies between observations and incidents in the health and safety domain
- Author
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Anastasiia Pika, Arthur H. M. ter Hofstede, Artem Polyvyanyy, and Moe Thandar Wynn
- Subjects
Knowledge management ,causality ,Computer science ,Big data ,0211 other engineering and technologies ,Process mining ,02 engineering and technology ,Occupational safety and health ,Domain (software engineering) ,big data ,cause of incidents ,021105 building & construction ,Energy company ,0501 psychology and cognitive sciences ,health and safety ,Safety, Risk, Reliability and Quality ,050107 human factors ,Management practices ,080600 INFORMATION SYSTEMS ,business.industry ,05 social sciences ,process mining ,Public Health, Environmental and Occupational Health ,data mining ,Causality ,proximity of events ,business ,Safety Research - Abstract
The paper at hand motivates, proposes, demonstrates, and evaluates a novel systematic approach to discovering causal dependencies between events encoded in large arrays of data, called causality mining. The approach has emerged in the discussions with our project partner, an Australian public energy company. It was successfully evaluated in a case study with the project partner to extract valuable, and otherwise unknown, information on the causal dependencies between observations reported by the company’s employees as part of the organizational health and safety management practices and incidents that had occurred at the organization’s sites. The dependencies were derived based on the notion of proximity of the observations and incidents. The setup and results of the evaluation are reported in this paper. The new approach and the delivered insights aim at improving the overall health and safety culture of the project partner practices, as they can be applied to caution and, thus, prevent future incidents.
- Published
- 2019