III: Small: Personalized Inconsistency Resolution in Online Databases
III: Small: Personalized Inconsistency Resolution in Online Databases
批准号:
1117527
负责人:
Alin Deutsch
金额:
$50.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2015-07-31
中文摘要
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英文摘要
The RICOLLA project manages inconsistencies in structured databases maintained collaboratively by an online community. RICOLLA remains fully functional in the presence of inconsistencies, enabling "resolve-as-you-go" consistency. RICOLLA allows users to collaboratively resolve certain conflicts while disagreeing on others. Building Ricolla involves the following technical contributions: a) a novel architecture that tolerates inconsistency, allows data query and update, while aiding inconsistency resolution by community members; b) a data model and interface for explaining the inconsistencies to the users; c) a set of resolution actions that allow each user to resolve individual data inconsistencies; d) a resolution policy language for summarizing a set of resolution actions based on high level criteria; and e) a set of algorithms for implementing the system on top of a relational database management system.The resulting techniques and prototype contribute to the infrastructure for the next generation of online databases. This benefits a variety of online communities who need to collaboratively edit structured data, ranging from the scientific domain to digital government and social networks. RICOLLA's evaluation includes as use cases two scientific communities (biologists and geoscientists) and UCSD students taking database classes. Direct deployment in teaching serves to both improve students' online collaboration and collect their feedback for RICOLLA's evaluation and tuning purposes. Publications, technical reports, software and experimental data resulting from this research are available at the project web site http://db.ucsd.edu/ricolla.
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CSR: Small: Scalable Fine-Grain Lineage for Debugging Data-Intensive Workflows
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DC: Large: Collaborative Research: ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis
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依托单位:
国内基金
海外基金
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