Research In Probabilistic Database Systems

许多数据库应用程序中都充斥着不确定或不完整的数据。例如，从科学仪器收到的数据可能包含测量误差，市场预测可能确定几个可行的方案，医疗诊断测试结果可能表明患者可能患有几种疾病，等等。然而，现有的大多数数据库系统将所有数据都视为“正确”，可能会导致无效的结论。这个项目研究的是不确定数据的管理。特别是，开发了一种新的概率数据模型(作为关系模型的扩展)，其中概率或置信度可以与属性值相关联。这些概率以离散概率分布函数的形式给出，其中该函数可能不完全指定。人们认为，这种模型为描述不确定的现实世界提供了一种自然的形式主义。运算符是用来处理概率数据的。它们包括类似于传统关系运算符的运算符，以及用于处理概率(例如，组合同一属性的两个分布)的新运算符。通过实际算例对模型和算子进行了验证。设计了一个使用概率数据模型的原型数据库管理系统，作为进一步研究的基础，并将在未来几年实施。

Uncertain or incomplete data abounds in many database applications. For example, data received from a scientific instrument may contain measurement errors, market forecasts may identify several feasible scenarios, medical diagnostic test results may indicate several probable illnesses for the patient, and so on. However, most existing database systems treat all data as "correct", possibly leading to invalid conclusions. This project investigates management of uncertain data. In particular, a new probabilistic data model is developed (as an extension to the relational model), where probabilities or confidences can be associated with the values of attributes. These probabilities are given in the form of a discrete probability distribution function where the function may be incompletely specified. It is believed that such model provides a natural formalism for describing the uncertain real world. Operators are developed for manipulating probabilistic data. They include operators that are analogous to conventional relational operators, as well as new operators for dealing with probabilities (e.g., combining two distributions for the same attribute). The model and the operators are validated using real world examples. A prototype database management system that uses the probabilistic data model is designed to serve as a basis for further research and to be implemented in future years.