Interactive multivariate data exploration for risk-based decision making

Silvia Oliveros-Torres, Purdue University

Abstract

Risk-based decision making is a data-driven process used to gather data about outcomes, analyze different scenarios, and deliver informed decisions to mitigate risk. An interactive visual analytics system can help derive insights from large amounts of data and facilitate the risk management process thereby providing a suitable solution to examine different decision factors. This work introduces two separate systems that incorporate visual analytics techniques to help the users in the tasks of identifying patterns, finding correlations, designing mitigation strategies, and assisting in the long term planning and assessment process. The first system looks at the National Health and Nutrition Examination survey (NHANES) dataset to help researchers explore patterns and form hypothesis about the current state of nutrition and health of the general US population. The second system introduces the application of integrated visual analytics techniques to explore risk from both strategic and operational aspects in the US Coast Guard Operations. The system performs the following tasks: the identification of risk priority areas, a comparative visualization of risk factors along with trend analysis, the distribution of pre-computed risk values, and the analysis of coverage efficiency. Both systems were developed through a collaborative user-centered process which allows us to reflect on the case study methodology for risk-based visual analytics systems.

Degree

M.S.E.C.E.

Advisors

Ebert, Purdue University.

Subject Area

Computer Engineering|Computer science

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