检测提取视图的差异化维护的机会
Detecting Opportunities for Differential Maintenance of Extracted Views
摘要：
半结构化和非结构化的数据管理具有挑战性，但遇到的许多问题与关系上下文中已经解决的问题类似。例如，在信息抽取领域，从工程特设的、应用特定的提取规则向使用表达性语言（如CPSL和AQL）的转变为提出可应用于广泛的提取程序的解决方案创造了机会。在这项工作中，我们专注于提取视图维护，一个问题，这是动机良好，彻底解决关系设置。特别是，我们正式和解决的问题，保持提取的关系与源文件，可以任意更新一致。我们正式描述了三类文档更新，即那些不相关的，自主计算的，和伪不相关的相对于一个给定的提取器。最后，我们提出了算法来检测伪不相关的文档更新相对于提取器表示为文档spectrum，一个模型的信息抽取的启发SystemT。
英文摘要：
Semi-structured and unstructured data management is challenging, but many of the problems encountered are analogous to problems already addressed in the relational context. In the area of information extraction, for example, the shift from engineering ad hoc, application-specific extraction rules towards using expressive languages such as CPSL and AQL creates opportunities to propose solutions that can be applied to a wide range of extraction programs. In this work, we focus on extracted view maintenance, a problem that is well-motivated and thoroughly addressed in the relational setting. In particular, we formalize and address the problem of keeping extracted relations consistent with source documents that can be arbitrarily updated. We formally characterize three classes of document updates, namely those that are irrelevant, autonomously computable, and pseudo-irrelevant with respect to a given extractor. Finally, we propose algorithms to detect pseudo-irrelevant document updates with respect to extractors that are expressed as document spanners, a model of information extraction inspired by SystemT.
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