A Knowledge Discovery Framework for Civil Infrastructure Contexts

提案：CMS-9987871PI：James Garrett Institution：Carnegie Mellon University日期：2001年4月20日ABSTRACT CMS-9987871“民用基础设施上下文的知识发现框架”PI：Carnegie Mellon University的Garrett，James；Carnegie Mellon University的Christos Faloutsos；以及芝加哥的伊利诺伊大学的Sue McNeil。这项研究的主要目标是将数据库中的知识发现(KDD)社区内开发的抽象CRISP-DM(跨行业标准数据挖掘)过程模型专门化，成为用于民用基础设施问题领域的框架。随着最近领域数据的积累，民用基础设施研究人员转向数据密集型技术，以帮助他们了解恶化机制和使用模式。大约在同一时间段，机器学习、数据库和统计社区的研究人员开始开发一套新的工具和技术，称为CRISP-DM，用于分析非常大的数据库。这一框架将协助民用基础设施研究人员系统地应用CRISP-DM过程来满足其数据分析需求。随着民用基础设施研究人员开始分析他们收集的大量基础设施数据，这样的框架将变得至关重要。研究小组将确定使用CRISP-DM过程分析民用基础设施的初步案例研究。案例研究将根据其知识发现问题的独特性进行选择。将特别注意那些在数据质量和数据准备方面提出具有挑战性问题的案例研究，这些问题是这一过程中最耗时和最困难的阶段，也是研究最少的阶段。研究小组将根据CRISP-DM过程特征对民用基础设施数据分析需求进行分类。将讨论该过程的所有阶段，但将深入讨论数据理解(包括数据质量)、数据准备和建模阶段。然后，研究小组将开发一个更具体的框架，将CRISP-DM过程应用于民用基础设施分析需求。研究小组还将确定并进行案例研究，以验证该框架。最后，将在最后一年完成基于这项研究的网上课程和网上资料库的开发和部署工作。

Proposal: CMS-9987871PI: James GarrettInstitution: Carnegie Mellon UniversityDate: April 20, 2001ABSTRACT CMS-9987871 " A Knowledge Discovery Framework for Civil Infrastructure Contexts" PI: Garrett, James, Carnegie Mellon University; Christos Faloutsos, Carnegie Mellon University; and Sue McNeil, University of Illinois at Chicago.The primary objective of this research is to specialize the abstract CRISP-DM (Cross-Industry Standard Process for Data Mining) process model being developed within the Knowledge Discovery in Databases (KDD) community, into a framework for use in civil infrastructure problem domains. With the recent accumulation of domain data, civil infrastructrue researchers have turned to data-intensive techniques to aid their understanding of deterioration mechanisms and usage patterns. During roughly the same time period, researchers in the machine learning, database, and statistical communities began to develop a set of new tools and techniques, known as CRISP-DM, to analyze very large databases. This framework will assist civil infrastructure researchers in systematically applying the CRISP-DM process for their data analysis needs. Such a framework will become vital for civil infrastructure researchers as they begin to analyze the enormous amounts of infrastructure data they have collected. The research team will identify the analyze preliminary case studies in civil infrastructure using the CRISP-DM process. Case studies will be chosen based on the uniqueness of their KDD problem characteristics. Special attention will be paid to those case studies that present challenging issues in data quality and data preparation, the most time-consuming and difficult stages of the process as well as the least-studied. The research team will classify civil infrastructure data analysis needs in terms of CRISP-DM process characteristics. All phases of the process will be addressed, but the data understanding (including data quality), data preparation, and modeling phases will be treated in-depth. The research team will then develop a more specific framework for applying the CRISP-DM process to civil infrastructure analysis needs. The research team will also identify and conduct case studies with which to validate the framework. Finally, the development and deployment of a web-based course and a web repository based on this research will be completed during the final year.