Crowdsourced Data Cleaning

众包数据清理

• 批准号: RGPIN-2016-05555
• 负责人: Wang, Jiannan
• 金额: $ 2.62万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634772
• 关键词: Crowdsourced; Data; Cleaning

Data cleaning is the process of detecting and correcting dirty (inconsistent, inaccurate, or incomplete) values from a database. Real-world data is often dirty. Analyses without data cleaning can be very risky, which may result in poor decision-making, and have a significant negative impact on applications. For example, in 2014, Statistics Canada under-reported the country’s job creation in July by more than 41000, based on an analysis of dirty data. This news was reported by hundreds of TV and Internet Media outlets around the world, resulting in various negative effects on Canada.

数据清理是检测和纠正数据库中脏值（不一致、不准确或不完整）的过程。现实世界的数据通常是脏的。没有数据清理的分析可能非常危险，这可能会导致糟糕的决策，并对应用程序产生重大的负面影响。例如，2014年，加拿大统计局根据对脏数据的分析，少报了该国7月份创造的就业机会超过41,000个。这一消息被全球数百家电视和网络媒体报道，对加拿大造成各种负面影响。