EAGER: Full Disclosure of Data Preparation and Use in Retrospective Studies

EAGER：全面披露回顾性研究中的数据准备和使用

• 批准号: 0954268
• 负责人: Lois Delcambre
• 金额: --
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0954268&HistoricalAwards=false
• 关键词: EAGER; Full; Disclosure; Data; Preparation

Although an overnight shipping company allows one to track the shipment of a package from coast to coast and a credit card company can track your purchases throughout the world, integrating, cleaning and using the various kinds of data available for engineering and planning studies in health, transportation and public infrastructure systems is extremely difficult. Such "retrospective" studies require intense scrutiny of the data and involve a myriad of decisions concerning the data and the definitions of the concepts involved to properly "clean" or prepare the data for the study. These decisions are typically written in English, if at all, and thus not automatically processable by any future user of the data. In a similar way, integrating data from multiple sources is difficult because of the details of the technology used to capture the data. The critical problems are that data from multiple sources can be hard to integrate and studies are almost never able to use data (that was carefully cleaned and scrutinized for one study) in another study. This seriously limits the opportunity to conduct larger or broader scale studies or to "re- run" studies on new data. One can envision a world where retrospective studies can be easily described, decisions about data use, data cleaning, and data integration can be precisely recorded, and operations, management, engineering, and planning activities can be informed by the results from studies as easily as we can currently track our packages as they wend their way across the country. This project envisions one solution to these problems; to devise methods and tools that will declaratively (i.e., at a high level, described in a formal language) document data manipulation activities in retrospective studies. Analysts will then be able to use this declarative specification to greatly facilitate the specification and conduct of new studies, making their jobs much easier. For example, if an analyst wants to repeat a study with new data, or with different parameters, the analyst will be able to use the declarative specifications saved from the original study, instead of having to decipher the low-level notes which may or may not have been saved from the original study. The declarative specifications will also be useful for combining the results of previous studies to create new studies. This project will focus three kinds of retrospective research studies: 1) studies from the Clinical Outcomes Research Institute (CORI), based on data concerning patient endoscopic procedures, 2) studies based on the Portland Oregon Regional Transportation Archive Listing (PORTAL), data containing years of highway loop-detector data as they conduct a study to determine the factors that correlate with certain kinds of congestion, and 3) studies based on data from the Portland, Oregon Water Bureau containing 8 years of water consumption data reported every 15 minutes from households across the city. Intellectual MeritBy attempting to bring the capability of state-of-the-art schema and data integration and data cleaning systems into a set of tools that can be used easily by analysts and can be interfaced seamlessly with existing analysis tools, the research team will make contributions to the database field by identifying separable concerns (within integration and cleaning) and by generalizing functions that are currently available in more complex, all encompassing tools. Broader Impacts The results of this project will have broad impact because they are advancing the science of retrospective studies significantly. The results will be applicable beyond the three areas being studied and will enable researchers and analysts to perform their studies more efficiently and to perform more studies. Results will be disseminated broadly, not only by the PI and co-PI through the usual publication venues, but by researchers in the three areas being studied. Major Themes/Keywords: Computer Science/Information Technology. Engineering. Social Science. Intelligent Transportation Systems. Health Systems. Water Consumption.

虽然隔夜运输公司可以跟踪从海岸到海岸的包裹运输，信用卡公司可以跟踪您在世界各地的购买情况，但整合，清理和使用各种数据可用于健康，运输和公共基础设施系统的工程和规划研究是非常困难的。这种“回顾性”研究需要对数据进行严格的审查，并涉及大量关于数据的决定和所涉概念的定义，以适当地“清理”或准备研究数据。这些决定通常是用英语写的，因此不能由数据的任何未来用户自动处理。同样，由于用于捕获数据的技术的细节，集成来自多个源的数据也很困难。关键的问题是，来自多个来源的数据可能很难整合，研究几乎永远无法在另一项研究中使用数据（为一项研究仔细清理和仔细检查）。这严重限制了进行更大或更广泛规模研究或对新数据“重新运行”研究的机会。人们可以想象这样一个世界：回顾性研究可以很容易地描述，关于数据使用、数据清理和数据集成的决策可以被精确地记录，运营、管理、工程和规划活动可以通过研究结果得到信息，就像我们目前可以在全国范围内跟踪我们的软件包一样容易。该项目设想了这些问题的一个解决方案;设计方法和工具，将声明（即，在一个高层次上，用一种正式的语言描述）在回顾性研究中记录数据操作活动。然后，分析师将能够使用这种声明性规范来极大地促进新研究的规范和实施，使他们的工作变得更加容易。例如，如果分析员想要使用新数据或不同参数重复研究，则分析员将能够使用从原始研究中保存的声明性规范，而不必破译可能从原始研究中保存或可能没有保存的低级注释。声明性规范也将有助于结合以前的研究结果，以创建新的研究。本项目将集中三种回顾性研究：1）来自临床结果研究所（科里）的研究，基于关于患者内窥镜手术的数据，2）基于波特兰俄勒冈州地区交通档案列表（PORTAL）的研究，数据包含多年的高速公路环路检测器数据，因为他们进行研究以确定与某些类型的拥堵相关的因素，以及3）基于来自俄勒冈州波特兰水务局的数据的研究，该数据包含来自整个城市的家庭每15分钟报告的8年的水消耗数据。智力优势通过尝试将最先进的模式和数据集成以及数据清理系统的能力引入一套分析人员可以轻松使用并可以与现有分析工具无缝连接的工具中，研究团队将通过识别可分离的关注点为数据库领域做出贡献（在集成和清理中），并通过一般化目前在更复杂的、无所不包的工具中可用的功能。更广泛的影响该项目的结果将产生广泛的影响，因为他们正在推进回顾性研究的科学显着。研究结果将适用于正在研究的三个领域之外，并将使研究人员和分析人员能够更有效地进行研究，并进行更多的研究。研究结果将被广泛传播，不仅由PI和co-PI通过通常的出版场所，而且由正在研究的三个领域的研究人员传播。主要主题/关键词：计算机科学/信息技术。工程.社会科学。智能交通系统。卫生系统。按用水量