Citation

Polyvyanyy A, Pika A, Wynn MT, Ter Hofstede AHM. Safety Sci. 2019; 118: 345-354.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.ssci.2019.04.045

PMID

unavailable

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.

Language: en

Keywords

Big data; Causality; Cause of incidents; Data mining; Health and safety; Process mining; Proximity of events