III: Small: Collaborative Research: Conflicts to Harmony: Integrating Massive Data by Trustworthiness Estimation and Truth Discovery

三：小：协同研究：从冲突到和谐：通过可信度估计和真相发现整合海量数据

• 批准号: 1320617
• 负责人: Jiawei Han
• 金额: $ 21.11万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320617&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Conflicts

Big data leads to big challenges, not only in the volume of data but also in its dynamics and variety. Multiple descriptions about the same set of objects or events from different sources unavoidably lead to data or information inconsistency. Then, among conflicting pieces of data or information, it is crucial to tell which data source is reliable or which piece of information is correct. Accurate information is referred to as the truth and the chance of a source providing accurate information is denoted as source reliability or trustworthiness. The objective of this project is to detect truths without supervision, by integrating source reliability estimation and truth finding. A unified framework is developed to model complex trustworthiness factors, heterogeneous data types, incremental and parallel computation, and source and data dependencies so that truth and trustworthiness can be inferred from multiple conflicting sources of heterogeneous, disparate, correlated, gigantic, scattered, and streaming data.This project makes tangible contributions to data integration, information understanding and decision making, and benefits many applications where critical decisions have to be made based on the correct information extracted from diverse sources. Research results of this project are integrated into course materials and projects, and into training students and new generation researchers, especially female and minority students. For further information about this project, please refer to the project website: http://www.cse.buffalo.edu/~jing/truth.htm

大数据带来了巨大的挑战，不仅体现在数据量上，还体现在数据的动态性和多样性方面。来自不同来源的对同一组对象或事件的多种描述不可避免地会导致数据或信息的不一致。那么，在相互冲突的数据或信息中，判断哪个数据源可靠或哪个信息正确就至关重要。准确的信息被称为真相，来源提供准确信息的机会被表示为来源可靠性或可信度。该项目的目标是通过集成源可靠性估计和真相发现来在没有监督的情况下检测真相。开发了一个统一的框架来对复杂的可信度因素、异构数据类型、增量和并行计算以及源和数据依赖性进行建模，以便可以从异构、不同、相关、庞大、分散和流数据的多个冲突源中推断出真实性和可信度。该项目对数据集成、信息理解和决策做出了切实的贡献，并使许多必须基于这些数据做出关键决策的应用程序受益。 从不同来源提取的正确信息。该项目的研究成果被纳入课程材料和项目，并培训学生和新一代研究人员，特别是女性和少数民族学生。有关该项目的更多信息，请参阅项目网站：http://www.cse.buffalo.edu/~jing/truth.htm