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首页> 外文期刊>International Journal of Environmental Research and Public Health >Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management
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Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management

机译：通过数据分析（KDDA）对环境风险管理的知识发现的问题制定

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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.
机译：随着环境风险管理领域的数据分析和数据科学越来越受欢迎，通过数据分析（KDDA）进程的正式知识发现，该过程纳入了特定环境风险管理问题的所有适用的分析技术至关重要。在这一新兴领域，研究了有限的研究，涉及使用决策支持来引出环境风险管理（ERM）目标并确定ERM决策者的分析目标。在本文中，我们解决了KDDA过程的ERM理解阶段的问题制定。我们建立一个DM3本体，以捕捉ERM目标并引起推断分析目标和相关的分析技术。开发了一种协助解决问题制定过程决策的框架。显示了基于本体的知识系统如何提供结构化指导，以在问题制定期间检索相关知识。强调，不仅重点在现实世界环境中运作KDDA方法，而且重点评估所提出的程序的有效性的重要性。我们展示了本地介绍如何通过基于城市化（以及相关经济活动水平）和社会经济剥夺程度的多级分析来概念危险空气污染物（HAPS）曝光变换以及社会经济剥夺程度（SED ）在当地邻里。 HAPS案例不仅突出了复杂性在问题制定中的作用，而且需要将数据集成在多种来源和采用适当的KDDA建模技术的重要性。 KDDA的挑战和机会总结了强调环境风险管理和哈哈普。

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