I-Corps: Probabilistic artificial intelligence (AI)-based software for proactive data quality assessment

I-Corps：基于概率人工智能 (AI) 的软件，用于主动数据质量评估

• 批准号: 2127797
• 负责人: Tahir Ekin
• 金额: $ 5万
• 依托单位: Texas State University - San Marcos
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-15 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127797&HistoricalAwards=false
• 关键词: Corps; Probabilistic; artificial; intelligence; AI

The broader impact/commercial potential of this I-Corps project is the development of a solution that identifies and corrects data data entry errors. The accelerating adoption of e-commerce and digital systems has increased the use of digital data entry systems. This may create challenges for providing equitable service to people with low income and education levels. The proposed data quality software finds and eliminates user errors at the point of data entry, providing proactive, secure and convenient support for users while filling in forms. The solution may also provide accurate data for companies. Compared to existing data quality solutions, this product eliminates the need for time-consuming and costly post data collection cleaning. The solution may benefit data-centric domains such as health care, finance, e-commerce, and tax and government benefit application systems where data errors can be costly. The goal is to provide users with proactive help that may reduce incorrect processing and potential issues with timely access. This proposed real-time, error alert and correction technology may benefit data entry specialists and consumers through corrective capability, while the companies that process the digital documents and online forms/web submissions may benefit from the cost-savings in terms of both post-submission corrective costs and potential damage control costs.This I-Corps project is based on the development of an artificial intelligence-driven analytical process and smart software that helps users submit mistake-free information at the time of data entry. The algorithms analyze the data to reveal unusual entries through the use of basic format checks, data matching, and probabilistic machine learning algorithms. Context based and personalized probabilistic analytical methods enable both variable and data accuracy assessment. The Bayesian nature of these algorithms allows incorporation of expert opinion and user feedback. The proposed software provides an interface that uses the analytical output and works with the user to verify their data entry before submission. This interface helps users fix mistakes at the time of data entry. Use of probabilistic algorithms allow the use of relative occurrence weights instead of computing over the whole data base. This makes the process faster with diminished privacy and security concerns over raw data transfer. The proposed technology involves multiple research areas including Bayesian statistics, data analytics, and software development. This project aims to verify a secure, trustworthy, and validated approach based on a proactive data quality paradigm and a suite of algorithms including probabilistic methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目更广泛的影响/商业潜力是开发一种解决方案，查明和纠正数据输入错误。电子商务和数字系统的加速采用增加了数字数据输入系统的使用。这可能对向低收入和低教育水平的人提供公平服务造成挑战。拟议的数据质量软件在数据输入时发现并消除用户错误，在用户填写表格时为用户提供主动、安全和方便的支持。 该解决方案还可以为公司提供准确的数据。与现有的数据质量解决方案相比，该产品无需进行耗时且昂贵的数据收集后清理。该解决方案可以使以数据为中心的领域受益，例如医疗保健、金融、电子商务以及税收和政府福利应用系统，其中数据错误可能会造成高昂的代价。目标是为用户提供主动帮助，以减少错误处理和及时访问的潜在问题。这种提出的实时错误警报和校正技术可以通过校正能力使数据输入专家和消费者受益，而处理数字文档和在线表格/网络提交的公司可能会从提交后纠正成本和潜在损害控制成本方面的成本节约中受益。这个I-Corps项目是基于人工智能的发展-驱动的分析过程和智能软件，帮助用户在数据输入时提交无错误的信息。这些算法通过使用基本格式检查、数据匹配和概率机器学习算法来分析数据，以揭示不寻常的条目。基于上下文和个性化的概率分析方法使变量和数据准确性评估成为可能。这些算法的贝叶斯性质允许结合专家意见和用户反馈。拟议的软件提供了一个界面，使用分析输出，并与用户合作，在提交之前验证其数据输入。这个界面可以帮助用户在输入数据时纠正错误。使用概率算法允许使用相对发生权重，而不是在整个数据库上进行计算。这使得过程更快，减少了对原始数据传输的隐私和安全问题。该技术涉及多个研究领域，包括贝叶斯统计，数据分析和软件开发。该项目旨在验证一种基于主动数据质量范式和一套算法（包括概率方法）的安全、可信和有效的方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。