Integrating data from disparate sources: a mass collaboration approach

R. McCann, Alexander Kramnik, Warren Shen, V. Varadarajan, Olu Sobulo, A. Doan
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引用次数: 29

Abstract

The rapid growth of distributed data at enterprises and on the WWW has fueled significant interest in building data integration systems. Such a system provides users with a uniform query interface (called mediated schema) to a multitude of data sources, thus freeing them from manually querying each individual source. To address some problems in the MOBS (Mass Collaboration to Build Systems) project at the University of Illinois, we develop solutions that learn from the multitude of users in the integration environment to improve the accuracy of integration tools. The improved accuracy in turn can significantly reduce the workload of the system builder. In developing MOBS we address the following key challenges: (i) obtaining user participation, (ii) learning from user participation, and (iii) combining user answers.
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集成来自不同来源的数据:一种大规模协作方法
企业和WWW上分布式数据的快速增长激起了人们对构建数据集成系统的极大兴趣。这样的系统为用户提供了对大量数据源的统一查询接口(称为中介模式),从而使他们不必手动查询每个单独的数据源。为了解决伊利诺斯大学(University of Illinois)的MOBS(大规模协作构建系统)项目中的一些问题,我们开发了从集成环境中的大量用户中学习的解决方案,以提高集成工具的准确性。提高的准确性反过来又可以显著减少系统构建者的工作量。在开发MOBS时,我们解决了以下关键挑战:(i)获得用户参与,(ii)从用户参与中学习,(iii)结合用户的回答。
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