Learning and verifying quantified boolean queries by example

A. Abouzeid, D. Angluin, C. Papadimitriou, J. Hellerstein, A. Silberschatz
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引用次数: 72

Abstract

To help a user specify and verify quantified queries --- a class of database queries known to be very challenging for all but the most expert users --- one can question the user on whether certain data objects are answers or non-answers to her intended query. In this paper, we analyze the number of questions needed to learn or verify qhorn queries, a special class of Boolean quantified queries whose underlying form is conjunctions of quantified Horn expressions. We provide optimal polynomial-question and polynomial-time learning and verification algorithms for two subclasses of the class qhorn with upper constant limits on a query's causal density.
通过实例学习和验证量化布尔查询
为了帮助用户指定和验证量化查询(除了最专业的用户之外,这类数据库查询对于所有人来说都非常具有挑战性),可以询问用户某些数据对象是其预期查询的答案还是非答案。在本文中,我们分析了学习或验证qhorn查询所需的问题数量,qhorn查询是一类特殊的布尔量化查询,其基本形式是量化Horn表达式的连词。我们为qhorn类的两个子类提供了最优多项式问题和多项式时间学习和验证算法,这些算法在查询的因果密度上具有常数上限。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.40
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0.00%
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