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

基本信息

  • 批准号:
    1353606
  • 负责人:
  • 金额:
    $ 33万
  • 依托单位:
  • 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

项目摘要

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空前数量的数据有望彻底改变娱乐、商业、治理和科学的运作方式。虽然数据便宜而丰富,但其中许多数据的质量低于过去30年管理的精确数据。构建一个处理这种不精确数据的应用程序是很困难的:它要求开发人员处理标准数据管理挑战(例如,并发性和可伸缩性),同时处理不精确和不完整的数据,这通常使用统计或机器学习技术(例如,插值和分类)来完成。Hazy项目通过构建一个集成了关系数据库管理系统范例和统计机器学习技术的系统来解决这一挑战。该项目进行以下主要任务:(1)设计一种语言将这些技术与标准SQL集成,(2)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(3)发现在底层数据更改或更新时有效维护统计模型的技术。最终目标是一个系统,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允许用户使用比以往更复杂的统计处理来处理更大量的数据。这反过来又使各种领域的新应用成为可能,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web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践式课程的数据和基础设施。此外,这个基础设施将被用作扩展工作的一部分,使高中生能够访问数据分析工具。Hazy的源代码被公开,其结果在项目网站(http://www.cs.wisc.edu/hazy/)上发布。

项目成果

期刊论文数量(0)
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Christopher Re其他文献

Do Multimodal Foundation Models Understand Enterprise Workflows? A Benchmark for Business Process Management Tasks
多模式基础模型理解企业工作流程吗?
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Michael Wornow;A. Narayan;Ben T Viggiano;Ishan S. Khare;Tathagat Verma;Tibor Thompson;Miguel Angel Fuentes Hernandez;Sudharsan Sundar;Chloe Trujillo;Krrish Chawla;Rongfei Lu;Justin Shen;Divya Nagaraj;Joshua Martinez;Vardhan Agrawal;Althea Hudson;Nigam H. Shah;Christopher Re
  • 通讯作者:
    Christopher Re

Christopher Re的其他文献

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{{ truncateString('Christopher Re', 18)}}的其他基金

Collaborative Research: Hardware-Aware Matrix Computations for Deep Learning Applications
协作研究:深度学习应用的硬件感知矩阵计算
  • 批准号:
    2247015
  • 财政年份:
    2023
  • 资助金额:
    $ 33万
  • 项目类别:
    Standard Grant
AF: Medium: Collaborative Research: Beyond Sparsity: Refined Measures of Complexity for Linear Algebra
AF:媒介:协作研究:超越稀疏性:线性代数复杂性的精确度量
  • 批准号:
    1763315
  • 财政年份:
    2018
  • 资助金额:
    $ 33万
  • 项目类别:
    Continuing Grant
AF:III:Small:Collaborative Research: New Frontiers in Join Algorithms: Optimality, Noise, and Richer Languages
AF:III:Small:协作研究:连接算法的新领域:最优性、噪声和更丰富的语言
  • 批准号:
    1318205
  • 财政年份:
    2013
  • 资助金额:
    $ 33万
  • 项目类别:
    Standard Grant
AF:III:Small:Collaborative Research: New Frontiers in Join Algorithms: Optimality, Noise, and Richer Languages
AF:III:Small:协作研究:连接算法的新领域:最优性、噪声和更丰富的语言
  • 批准号:
    1356918
  • 财政年份:
    2013
  • 资助金额:
    $ 33万
  • 项目类别:
    Standard Grant
EAGER Collaborative: Bringing Together Computational and Linguistic Methods to Extract 'Dark' Geosciences Data for the EarthCube Framework
EAGER Collaborative:结合计算和语言方法为 EarthCube 框架提取“暗”地球科学数据
  • 批准号:
    1242902
  • 财政年份:
    2012
  • 资助金额:
    $ 33万
  • 项目类别:
    Standard Grant
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
  • 财政年份:
    2011
  • 资助金额:
    $ 33万
  • 项目类别:
    Continuing Grant

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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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