Design and Development of a Data Management System for Uncertain Data

当前的数据库管理系统要求根据精确的值对所有数据进行建模。然而，有大量的应用领域的数据值是不精确或不确定的。这些数据的例子包括传感器的测量值、运动物体的位置和实验数据。对于这些应用程序，需要开发一个支持不确定数据类型的数据库管理系统。该项目旨在开发一个全面的数据库管理系统，用于存储和查询不确定或不精确的数据。该项目包括基于关系模型为不确定数据创建一个全面的模型，扩展SQL以支持对不确定数据的概率查询，高效准确地评估概率查询的技术，以及原型系统的开发。具体的优化问题包括索引、连接算法和不确定数据的查询优化。原型将作为开源PostgreSQL数据库管理系统的扩展而开发。一个真实的移动物体的应用是针对原型的测试。此外，与生物学和化学专家的合作将作为这些领域发展的适用性的验证。预计该项目将对需要不确定数据管理系统的应用程序领域以及数据库社区产生重大影响。该提案旨在为多种类型的不确定性提供单一模型，并为不确定数据开发索引、连接和查询优化技术。该项目将通过学生参与研究项目、课程开发和研究研讨会来整合研究和教育。这项研究的结果将列入课程项目，并将通过同行评议的期刊和会议出版物、会议报告和也将提供原型传播的网页（http://wwww.cs.purdue.edu/homes/sunil/UncertainDB）进行传播。

Current database management systems require all data to be modeled in terms of precise values. However, there is a large number of application domains where data values are imprecise or uncertain. Examples of such data include measurements for sensors, locations of moving objects, and experimental data. For these applications there is a need to develop a database management system that supports uncertain data types. The project aims to develop a comprehensive database management system for storing and querying uncertain, or imprecise data. The project encompasses the creation of a comprehensive model for uncertain data based upon the relational model, the extension of SQL to support probabilistic queries over uncertain data, techniques for efficient and accurate evaluation of probabilistic queries, and the development of a prototype system. The specific optimization issues addressed include indexing, join algorithms, and query optimization for uncertain data. The prototype will be developed as an extension of the open-source PostgreSQL database management system. A realistic moving objects' application is targeted for testing of the prototype. In addition, collaboration with experts in biology and chemistry will serve as validations of the applicability of the developments in these domains. The project is expected to have a significant impact on application domains that are in need of an uncertain data management system, and also on the database community. The proposal is expected to provide a single model for multiple types of uncertainty, and to develop indexing, join, and query optimization techniques for uncertain data. This project will integrate research and education through student participation in research projects, course development, and research seminar. The results from this research will be included in course projects and will be disseminated via peer-reviewed publications in journals and conferences, conference presentations, and the Web pages (http://wwww.cs.purdue.edu/homes/sunil/UncertainDB) that will also provide the prototype dissemination.