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: 1353606
• 负责人: Christopher Re
• 金额: $ 33万
• 依托单位: Stanford University
• 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: 2013-07-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1353606&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/).

个人、公司、政府和科学家可以获得的前所未有的数据量有望彻底改变娱乐、商业、治理和科学的运作方式。尽管数据廉价且丰富，但其中大部分数据的质量低于过去 30 年管理的精确数据。构建处理这种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），同时处理不精确和不完整的数据，这通常是使用统计或机器学习来完成的技术（例如插值和分类）。 Hazy 项目通过构建一个将关系数据库管理系统范例与统计机器学习技术相集成的系统来解决这一挑战。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）提出一种代数来实现该语言并支持自动优化（类似于标准 RDBMS），以及（III）发现在基础数据发生更改或更新时有效维护统计模型的技术。最终目标是建立一个系统，使开发使用不精确数据的可扩展应用程序与开发精确对应的应用程序一样容易。 Hazy 允许用户通过比以往更复杂的统计处理来处理更大量的数据。反过来，这又可以在多个领域实现新的应用，例如生命和物理科学传感​​应用、医疗保健和环境监测以及基于企业和基于网络的信息提取。该项目的研究用于开发威斯康星大学麦迪逊分校正在开发的新实习式课程的数据和基础设施。此外，该基础设施将用作外展工作的一部分，使高中生能够访问数据分析工具。 Hazy 的源代码已开源，结果在项目网站 (http://www.cs.wisc.edu/hazy/) 上传播。