Feedback-based annotation, selection and refinement of schema mappings for dataspaces
Feedback-based annotation, selection and refinement of schema mappings for dataspaces
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DOI：
10.1145/1739041.1739110
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发表时间：
2010-03
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影响因子：
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通讯作者：
Khalid Belhajjame;N. Paton;S. Embury;A. Fernandes;Cornelia Hedeler
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作者：
Khalid Belhajjame;N. Paton;S. Embury;A. Fernandes;Cornelia Hedeler
模式映射的规范已被证明是耗时和资源消耗，并已被公认为是一个关键的瓶颈，大规模部署的数据集成系统。在试图解决这个问题，数据空间已被提出作为一个数据管理抽象，其目的是减少所需的前期成本，建立一个数据集成系统，通过逐步指定模式映射，通过与最终用户的互动，按需付费的方式。作为这个方向的一步，我们探索一种方法，增量注释模式映射使用从最终用户获得的反馈。在这样做的时候，我们并不期望用户检查映射规范;相反，他们对使用映射评估的查询的结果进行评论。使用注释计算的基础上，用户的反馈，我们提出了一种方法，用于选择从一组候选映射，那些要用于查询评估考虑用户的要求，在精度和召回。在此过程中，我们将映射选择视为优化问题。映射注释可能揭示模式映射的质量很差。我们还展示了如何反馈可以用来支持更好的质量映射从现有的映射通过细化的派生。一个进化算法是用来高效和有效地探索大空间的映射，可以通过细化。评估结果表明，我们的解决方案的有效性注释，选择和细化模式映射。
The specification of schema mappings has proved to be time and resource consuming, and has been recognized as a critical bottleneck to the large scale deployment of data integration systems. In an attempt to address this issue, dataspaces have been proposed as a data management abstraction that aims to reduce the up-front cost required to setup a data integration system by gradually specifying schema mappings through interaction with end users in a pay-as-you-go fashion. As a step in this direction, we explore an approach for incrementally annotating schema mappings using feedback obtained from end users. In doing so, we do not expect users to examine mapping specifications; rather, they comment on results to queries evaluated using the mappings. Using annotations computed on the basis of user feedback, we present a method for selecting from the set of candidate mappings, those to be used for query evaluation considering user requirements in terms of precision and recall. In doing so, we cast mapping selection as an optimization problem. Mapping annotations may reveal that the quality of schema mappings is poor. We also show how feedback can be used to support the derivation of better quality mappings from existing mappings through refinement. An evolutionary algorithm is used to efficiently and effectively explore the large space of mappings that can be obtained through refinement. The results of evaluation exercises show the effectiveness of our solution for annotating, selecting and refining schema mappings.