CAREER: A Scalable, Declarative, Imprecise Database Management System

职业：可扩展、声明式、不精确的数据库管理系统

• 批准号: 1054009
• 负责人: Christopher Re
• 金额: $ 55万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1054009&HistoricalAwards=false
• 关键词: CAREER; Scalable; Declarative; Imprecise; Database

The unprecedented amounts of data available to individuals, companies, governments, and scientists promises to revolutionize the way entertainment, business, governance, and science operate. And while data are cheap and plentiful, much of this data is lower quality than the precise data that has been managed for the last 30 years. Building an application that processes this imprecise data is difficult: it requires that developers handle both standard data management challenges (e.g., concurrency and scalability), while at the same time coping with imprecise and incomplete data, which is typically done using statistical or machine learning techniques (e.g., interpolation and classification). The Hazy project addresses this challenge by building a system that integrates the paradigms of relational database management systems with statistical machine learning techniques. This project conducts the following major tasks: (I) designing a language to integrate these techniques with standard SQL, (II) proposing an algebra to implement this language along with support for automatic optimization (similar to a standard RDBMS), and (III) discovering techniques to efficiently maintain the statistical models as the underlying data are changed or updated. The end goal is a system that makes it as easy to develop scalable applications that use imprecise data as it is to develop their precise counterparts. Hazy allows users to process larger amounts of data with more sophisticated statistical processing than ever before. In turn, this enables new applications in a divese set of areas, such as life and physical science sensing applications, health-care and environmental monitoring, and enterprise-based and Web-based information extraction.The research of this project is used to develop the data and infrastructure for new practicum-style courses that are under development at the University of Wisconsin-Madison. In addition, this infrastructure will be used as part of an outreach effort to enable high school students to gain access to data analysis tools. The source code of Hazy is released into open source and the results are disseminated on the project Web site (http://www.cs.wisc.edu/hazy/).

为个人，公司，政府和科学家提供的前所未有的数据有望彻底改变娱乐，商业，治理和科学的方式。尽管数据廉价且丰富，但大部分数据质量低于过去30年来管理的确切数据。构建处理此不精确数据的应用程序很困难：它要求开发人员应对标准数据管理挑战（例如并发性和可扩展性），同时应对不准确和不完整的数据，这通常是使用统计或机器学习技术（例如插入和分类和分类）完成的。朦胧的项目通过建立一个将关系数据库管理系统范式与统计机器学习技术集成的系统来应对这一挑战。该项目执行以下主要任务：（i）设计一种语言，将这些技术与标准SQL集成，（ii）提出一个代数来实施该语言，并支持自动优化（类似于标准的RDBMS），（III）发现以有效地维持统计模型，因为它已更改了统计模型。最终目标是一个系统，它使开发可扩展的应用程序很容易使用不精确的数据，就像开发其精确的数据一样。 Hazy允许用户使用比以往任何时候都更复杂的统计处理处理大量数据。反过来，这可以在分区的一组领域中进行新的应用程序，例如生活和物理科学传感​​应用，医疗保健和环境监测以及基于企业的和基于Web的信息提取。该项目的研究用于开发有关威斯康星大学 - 威斯康星大学 - 威斯康星大学新实践型课程的数据和基础架构。此外，该基础架构将被用作推广工作的一部分，以使高中生能够访问数据分析工具。朦胧的源代码被发布到开源，并在项目网站（http://www.cs.wisc.edu/hazy/）上传播结果。