...
  • 主题
高级搜索  >
历史搜索 清空历史
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
【24h】

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

机译：通过数据分析（KDDA）进行知识发现以解决环境风险管理问题

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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 DM 3 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：随着数据分析和数据科学在环境风险管理领域的日渐普及，通过数据分析（KDDA）的正规知识发现流程必不可少，该流程结合了针对特定环境风险管理问题的所有适用分析技术。在这个新兴领域，关于使用决策支持来引发环境风险管理（ERM）目标并确定ERM决策者的分析目标的研究很少。在本文中，我们在KDDA流程的ERM理解阶段解决问题的提出。我们构建了一个DM 3本体来捕获ERM目标并推断分析目标和相关的分析技术。开发了一个框架来协助问题制定过程中的决策。它显示了基于本体的知识系统如何提供结构化指导，以在问题制定过程中检索相关知识。强调了不仅要在现实环境中操作KDDA方法，而且还要评估所提出程序的有效性的重要性。我们通过对城市化水平（及相关经济活动）和社会经济剥夺程度（SED）进行多层次分析，通过概念化有害空气污染物（HAP）暴露概念，论证了本体推理可用于发现分析目标和技术的方法）。在HAPs案例中，不仅强调了问题制定中复杂性的作用，而且还强调了整合多个来源的数据的必要性以及采用适当的KDDA建模技术的重要性。总结了KDDA的挑战和机遇，重点是环境风险管理和HAP。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号