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，系统在很大程度上依赖数据与操纵数据的软件一样。 与软件不同，数据不能轻易测试或分析以确保正确性。问题的一部分是很难确定数据是否错误。 印刷错误可以通过数量级来更改数据项。例如，当正确值为12.34时，可以输入数字1234。不幸的是，通过手动数据审核发现这种错误是繁重的，难以实现的。 数据错误可能是昂贵的：电子表格数据中的错误导致了数百万美元的损失，并且估计数据质量较差，每年使美国经济损失超过6000亿美元。数据调试是一种通过利用数据与在其上操作的程序之间的相互作用来定位可能数据错误的新方法。由于不可能知道数据是否不正确，因此数据调试旨在做下一个最好的事情：识别对计算有异常影响的数据。直观地，对计算结果产生过多影响的数据要么很重要，要么是错误的。相比之下，存在对最终结果没有特别异常影响的错误数据并不重要。 通过引起对数据的注意，数据调试可以提供对数据和计算的见解并揭示错误。数据调试特别适合数据密集型编程环境（例如数据库和电子表格）交织数据和程序。 数据调试可以大大减少人类数据输入错误或数据损坏的风险，提高计算对数据的可靠性，并可能节省美国经济的数百万美元。