Big data visualization for occupational health and security problem in oil and gas industry

Abstract

Association rule learning is a popular and well-researched set of methods for discovering interesting relations between entities in large databases in real-world problems. In this regard, an intelligent offshore oil industry environment is a very complex scenario and Occupational Health and Security (OHS) is a priority issue as it is an important factor to reduce the number of accidents and incidents records. In the oil industry, there exist standards to identify and record workplace accidents and incidents in order to provide guiding means on prevention efforts, indicating specific failures or reference, means of correction of conditions or circumstances that culminated in accident. OHS’s employees are in charge of analyzing the mined rules to extract knowledge. In most of cases these users has two main challenges during this process: (i) to explore the measures of interestingness (confidence, lift, support, etc.) and (ii) to understand and analyze the large number of association rules. In this sense, an intuitive visualization of mined rules becomes a key component in a decision-making process. In this paper, we propose a novel visualization of spatio-temporal rules that provides the big picture about risk analysis in a real world environment. Our main contribution lies in an interactive visualization of accident interpretations by means of well-defined spatio-temporal constraints, in the oil industry domain.