Crowdsourced Data Cleaning

数据清理是从数据库中检测和更正脏(不一致、不准确或不完整)值的过程。现实世界的数据往往是肮脏的。没有数据清理的分析可能会非常危险，这可能会导致糟糕的决策，并对应用程序产生重大负面影响。例如，2014年，根据对肮脏数据的分析，加拿大统计局少报了该国7月份的就业岗位，少报了41000以上。这一消息被世界各地数百家电视和互联网媒体报道，对加拿大造成了各种负面影响。 尽管在基于机器的数据清理技术方面已经有了很长一段时间的工作，但许多清理任务对于仅限于机器的解决方案来说太具有挑战性了。最近，众包技术和平台的进步(如Amazon Machine Turk)提供了一种非常有希望的方式，让人和计算机以低成本解决复杂的问题。鉴于这一巨大的机遇，本方案将研究众包数据清理，智能地将人和计算机结合起来，解决具有挑战性的数据清理问题。 这项工作不仅将开辟数据库领域的一个新的研究领域，而且还将惠及许多其他科学领域，如图书馆学或社会学，这些领域经常需要对真实世界的数据集进行数据分析。此外，随着大数据的兴起，世界正在走向一个更多由数据驱动的环境。长期以来，数据清理一直被认为是从数据中提取价值的瓶颈。众包数据清洗具有以较低的清洗成本显著提高数据质量的潜力，在这种新环境下将有越来越多的应用，例如清理客户信息以进行可靠的市场分析，以及清理患者的病史以进行准确的疾病诊断。

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. Although there has already been a long line of work on machine-based data cleaning techniques, many cleaning tasks are too challenging for machine only solutions. Recently, the advance of crowdsourcing techniques and platforms (e.g., Amazon Mechanical Turk) provides a highly promising way to involve humans and computers in solving complex problems at low cost. In view of this great opportunity, this proposal will study crowdsourced data cleaning, intelligently combining humans and computers to address challenging data-cleaning problem. This work will not only open up a new research area in the database field, but also benefit a lot of other scientific fields, such as library science or sociology, which often require to conduct data analysis on real-world datasets. Furthermore, with the rise of big data, the world is moving towards a more data-driven environment. Data cleaning has long been considered as a bottleneck for extracting value from data. Crowdsourced data cleaning, which has the potential of significantly improving data quality at low cleaning cost, will have an increasing number of applications in this new environment, such as cleaning customer information for reliable market analysis, and cleaning patients' medical history for accurate disease diagnosis.