Statistical Mining of Massive Data, Data Depth and Aviation Risk Management

海量数据统计挖掘、数据深度与航空风险管理

• 批准号: 0306008
• 负责人: Regina Liu
• 金额: $ 22万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0306008&HistoricalAwards=false
• 关键词: Statistical; Mining; Massive; Data; Depth

AbstractPI: Regina Liu, Proposal Number: DMS-0306008Title: Statistical Mining of Massive Data, Data Depth and Aviation Risk ManagementThis project aims to develop a systematic data mining procedure for exploring some large non-standard data sets by automatic means, with the purpose of discovering meaningful patterns and useful features. The procedure includes four particular research areas: text analysis, risk analysis, data depth and multivariate nonparametric analysis. The PI proposes to introduce and investigate several new data extracting and tracking methodologies. She plans to use two aviation safety report repositories ("Program Tracking Report Subsystem" from the FAA and "Aviation Accident Statistics" from NTSB) to illustrate problem statements as well as applications of the proposed research to aviation risk management. The data mining procedures and methods for constructing and tracking performance measures or risk indicators developed in this project can be a critical component of any effective decision-support systems. Also, included in this project are: a research plan for establishing a general theory of multivariate spacings based on data depth, and some new nonparametric statistical inference methods using the concept of depth-ranking.The recent advances in computing and data acquisition technologies have made the collection of massive amounts of data a routine practice in many fields. Besides the voluminous size, the types of data are also often less traditional. They may be textual, image, or unstructured high dimensional data. Scientists face increasingly the task of analyzing such massive non-standard data sets. Moreover, with the low cost of implementing automated data collection systems, many data collection systems are often designed to accumulate maximum amounts of data without clearly defined missions. Consequently, the data analysis required of statisticians often includes the new challenge of mining a sea of unstructured data. The goal of this project is to develop a comprehensive statistical mining scheme that should have a broad applicability to many fields. The investigator plans to use some aviation safety report repositories from the NTSB (National Transportation Safety Board) to illustrate problem statements as well as applications of the proposed research to aviation risk management. The data mining procedures and methods for constructing and tracking performance measures or risk indicators developed in this project can be a critical component of any effective decision-support systems.

摘要PI：Regina Liu，提案编号：DMS-0306008题目：海量数据的统计挖掘、数据深度和航空风险管理本项目旨在开发一个系统的数据挖掘程序，用于通过自动手段探索一些大型非标准数据集，目的是发现有意义的模式和有用的特征。该程序包括四个特定的研究领域：文本分析、风险分析、数据深度和多元非参数分析。PI建议引入和研究几种新的数据提取和跟踪方法。她计划使用两个航空安全报告库（“程序跟踪报告子系统”从FAA和“航空事故统计”从NTSB），以说明问题的陈述，以及拟议的研究应用到航空风险管理。本项目中开发的用于构建和跟踪业绩计量或风险指标的数据挖掘程序和方法可以成为任何有效决策支持系统的关键组成部分。同时，本项目还包括：建立基于数据深度的多元空间一般理论的研究计划，以及利用深度排序概念的一些新的非参数统计推断方法。近年来，计算和数据采集技术的进步使得大量数据的收集在许多领域成为一种常规做法。除了庞大的规模，数据类型也往往不那么传统。它们可以是文本、图像或非结构化的高维数据。科学家们越来越多地面临着分析如此庞大的非标准数据集的任务。此外，由于实施自动化数据收集系统的成本较低，许多数据收集系统往往被设计成在没有明确规定任务的情况下积累最大数量的数据。因此，统计人员所需的数据分析通常包括挖掘大量非结构化数据的新挑战。该项目的目标是开发一个全面的统计挖掘计划，应该有广泛的适用性，许多领域。研究人员计划使用NTSB（国家运输安全委员会）的一些航空安全报告库来说明问题陈述以及拟议研究在航空风险管理中的应用。本项目中开发的用于构建和跟踪业绩计量或风险指标的数据挖掘程序和方法可以成为任何有效决策支持系统的关键组成部分。