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II: Information Integration in the Presence of Uncertainty

II: Information Integration in the Presence of Uncertainty
II:不确定性下的信息整合
批准号:
0513877
负责人:
Dan Suciu
金额:
$0.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-08-01 至 2010-07-31

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中文摘要
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英文摘要
Experience building and fielding data integration systems has shown that they are brittle in a very fundamental way: they cannot handle uncertainty about data or about how data is combined to provide answers. This limitation is especially pronounced in scientific applications, where data is inherently uncertain and the models of the domain are constantly evolving. From the users' perspective, the inability to model uncertainty can result in loss of relevant answers, an explosion of irrelevant answers and in no justification of answers. The limitation is deeply rooted in the deterministic paradigm underpinning data management systems today, which is designed to support scalability to large data instances, but is incapable of representing and reasoning about uncertainty.A new approach to data integration, where uncertainties are handled explic-Itly, is proposed. Over the past few years, the BioMediator system, whichintegrates about a dozen public data sources on genes and proteins, has been available. The group has observed and documented the types of uncertainty that limit the power of any mediator-based integration system like BioMediator. These uncertainties occur at three levels: at the data instance level, at the schema level, and at the user query level. In the new approach, all uncertainties will be made explicit in the system, and represented in a uniform way, using a probabilistic data model. The mediator system supports a query language with SQL but with a modified semantics: the answers to each query are annotated with a probability score, and a lineageinformation.The new work will involve the design of a probabilistic data model, the development of probabilistic query processing and optimization techniques, and the design of user feedback methods. They will build a system, U2 (short for UII { UncertainInformation Integration ) that will model uncertainty at all levels of the system, including the query language, mediated schema, source mappings and source data. U2 will explain its results to the user and will actively seek to resolve uncertainty when it arises, incorporating feedback from the user where possible. They will extend the BioMediator System and collaborate with the current users of the system.There are three areas of broader impact. Issues of information integration will be integrated more tightly into the undergraduate and graduate database curriculumSecond, the research will fuel collaboration with biomedical computing research, and willextend the BioMediator system that is currently in use by practitioners in the field. Finally, tools and services will be made available for public use.
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III: Small: Datalog with Aggregates: Complexity, Optimization, Evaluation
  • 批准号:
    2314527
  • 项目类别:
    Standard Grant
  • 资助金额:
    $60.0万
  • 财政年份:
    2023
  • 负责人:
    Dan Suciu
  • 依托单位:
NSF-BSF: III: Small: Data Driven Schema
  • 批准号:
    2109922
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2021
  • 负责人:
    Dan Suciu
  • 依托单位:
III: Medium: Collaborative Research: Reasoning about Optimizers for Data-Intensive Systems
  • 批准号:
    1954222
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2020
  • 负责人:
    Dan Suciu
  • 依托单位:
III:Small: Optimal Query Processing meets Information Theory: from Proofs to Algorithms
  • 批准号:
    1907997
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2019
  • 负责人:
    Dan Suciu
  • 依托单位:
国内基金
海外基金
Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
Exploring the Intrinsic Mechanisms of CEO Turnover and Market Reaction: An Explanation Based on Information Asymmetry
  • 批准号:
    W2433169
  • 项目类别:
    外国学者研究基金项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    HAOFEI ZHANG
  • 依托单位:
SCIENCE CHINA Information Sciences