EAGER: Data Debugging

EAGER：数据调试

• 批准号: 1349784
• 负责人: Emery Berger
• 金额: $ 15万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1349784&HistoricalAwards=false
• 关键词: EAGER; Data; Debugging

Today, systems rely as heavily on data as on the software that manipulates that data. Unlike software, data cannot be easily tested or analyzed for correctness. Part of the problem is that it is difficult to decide whether data is wrong. Typographical errors can change data items by orders of magnitude. For example, the number 1234 might be entered when the correct value is 12.34. Unfortunately, finding this kind of mistake via manual data auditing is onerous, unscalable, and error-prone. Data errors can be costly: Errors in spreadsheet data have led to million dollar losses, and poor data quality has been estimated to cost the US economy more than $600 billion per year. Data debugging is a new approach for locating likely data errors by leveraging the interaction between data and the programs that operate on it. Since it is impossible to know a priori whether data is incorrect, data debugging aims to do the next best thing: identifying data that has an unusual impact on the computation. Intuitively, data with an inordinate impact on the result of a computation is either very important, or it is wrong. By contrast, wrong data whose presence has no particularly unusual effect on the final result does not matter. By calling attention to data with inordinately high impact, data debugging can provide insights into both the data and the computation and reveal errors. Data debugging is especially well-suited for data-intensive programming environments like databases and spreadsheets that intertwine data and programs. Data debugging can dramatically reduce the risks of human data entry errors or data corruption, increase the reliability of computations over data, and potentially save the US economy millions of dollars.

如今，系统对数据的依赖程度与对数据操作软件的依赖程度一样。 与软件不同，数据不能轻易地测试或分析其正确性。部分问题在于很难确定数据是否错误。 印刷错误可能会使数据项发生数量级的变化。例如，当正确值为 12.34 时，可能会输入数字 1234。不幸的是，通过手动数据审核发现此类错误非常繁重、不可扩展且容易出错。 数据错误可能代价高昂：电子表格数据中的错误已导致数百万美元的损失，据估计，糟糕的数据质量每年给美国经济造成超过 6000 亿美元的损失。数据调试是一种通过利用数据和对其进行操作的程序之间的交互来定位可能的数据错误的新方法。由于不可能先验地知道数据是否不正确，因此数据调试的目的是退而求其次：识别对计算有异常影响的数据。直观上，对计算结果影响过大的数据要么非常重要，要么就是错误的。相比之下，错误数据的存在对最终结果没有特别不寻常的影响并不重要。 通过引起人们对具有极高影响力的数据的关注，数据调试可以提供对数据和计算的洞察并揭示错误。数据调试特别适合数据密集型编程环境，例如将数据和程序交织在一起的数据库和电子表格。 数据调试可以显着降低人为数据输入错误或数据损坏的风险，提高数据计算的可靠性，并可能为美国经济节省数百万美元。