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Big data platform for health and safety accident prediction

Big data platform for health and safety accident prediction The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.Design/methodology/approachThe study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.FindingsThe proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.Originality/valueThis study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png World Journal of Science, Technology and Sustainable Development Emerald Publishing

Big data platform for health and safety accident prediction

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2042-5945
DOI
10.1108/wjstsd-05-2018-0042
Publisher site
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Abstract

The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.Design/methodology/approachThe study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.FindingsThe proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.Originality/valueThis study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.

Journal

World Journal of Science, Technology and Sustainable Development – Emerald Publishing

Published: Jan 7, 2019

Keywords: Big data analytics; Health and safety; Machine learning; Health hazards analytics

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