Uncertain Relational Database Management and Integration Systems: Concepts and Techniques

在不同领域的许多应用程序需要存储和处理大量的不确定数据的能力。虽然传统的数据库管理系统（DBMS）在处理精确数据和缺失值方面非常成功，但它们对不确定数据提供的支持很少或根本没有。还需要的是“量化”数据完整性的能力。非正式地说，一个数据是不确定的，如果它的真实性没有明确建立。 不确定性在数据库和人工智能社区中已经被广泛研究了二十多年，产生了大量的原则，概念，表示和算法。然而，还需要做更多的工作来开发更好的解决方案，以全面实现不确定数据管理。这解释了最近在数据库研究中对该主题的关注增加，以及本提案的动机。主要问题包括（1）语义（表示），（2）评估（正确和有效的处理），

Many applications in diverse domains require the ability to store and process voluminous uncertain data. While traditional database management systems (DBMS) have been quite successful in handling precise data and missing values, they offer little or no support for uncertain data. What is also needed is the ability to "quantify" the integrity of the data. Informally, a piece of data is uncertain if its truth is not established definitely. Uncertainty has been studied extensively for over two decades in database and AI communities, resulting in a substantial body of principles, concepts, representations, and algorithms. However, more work is required to develop better solutions for a full realization of uncertain data management. This explains recent increased attention to the subject in database research, and the motivation of this proposal. The major problems include (1) semantics (representations), (2) evaluation (correct and efficient processing),