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

PI：Regina Liu，提案编号：DMS-0306008标题：海量数据的统计挖掘、数据深度与航空风险管理本项目旨在开发一个系统的数据挖掘过程，以自动方式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、数据深度和多变量非参数分析。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(美国联邦航空局的“项目跟踪报告子系统”和NTSB的“航空事故统计”)来说明问题陈述以及拟议研究在航空风险管理中的应用。本项目开发的用于构建和跟踪业绩衡量或风险指标的数据挖掘程序和方法可以是任何有效的决策支持系统的关键组成部分。此外，该项目还包括：建立基于数据深度的多变量间隔一般理论的研究计划，以及使用深度排序概念的一些新的非参数统计推断方法。最近计算和数据采集技术的进步使收集大量数据成为许多领域的常规做法。除了庞大的规模之外，数据的类型也往往不那么传统。它们可以是文本、图像或非结构化高维数据。科学家们面临着越来越多分析如此海量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，许多数据收集系统的设计往往是为了在没有明确界定任务的情况下积累最大数量的数据。因此，统计学家所需的数据分析往往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方案，该方案应具有广泛的适用性。调查人员计划使用国家运输安全委员会(NTSB)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。本项目开发的用于构建和跟踪业绩衡量或风险指标的数据挖掘程序和方法可以是任何有效的决策支持系统的关键组成部分。

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.