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Int. J. Environ. Res. Public Health 2016, 13(12), 1245; doi:10.3390/ijerph13121245

Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management

1
Center for Information Systems and Technology (CISAT), Claremont Graduate University, 130 E. Ninth St. ACB225, Claremont, CA 91711, USA
2
Information Systems, Virginia Commonwealth University, Richmond, VA 23284, USA
3
Public Administration, University of Hawaii, West Oahu, Kapolei, HI 97607, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Yu-Pin Lin
Received: 5 September 2016 / Revised: 3 December 2016 / Accepted: 6 December 2016 / Published: 15 December 2016
(This article belongs to the Special Issue Ecological Economics, Environmental Health Policy and Climate Change)
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Abstract

With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. We build a DM3 ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. A framework to assist decision making in the problem formulation process is developed. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The HAPs case highlights not only the role of complexity in problem formulation but also the need for integrating data from multiple sources and the importance of employing appropriate KDDA modeling techniques. Challenges and opportunities for KDDA are summarized with an emphasis on environmental risk management and HAPs. View Full-Text
Keywords: Knowledge Discovery via Data Analytics (KDDA); problem formulation; decision support; environmental risk; ontology Knowledge Discovery via Data Analytics (KDDA); problem formulation; decision support; environmental risk; ontology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Y.; Thomas, M.; Osei-Bryson, K.-M.; Levy, J. Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management. Int. J. Environ. Res. Public Health 2016, 13, 1245.

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