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arXiv 2312.08038cs.DB
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面向一致操作型一致性查询应答的组合近似方法

Combined Approximations for Uniform Operational Consistent Query Answering

Marco Calautti, Ester Livshits, Andreas Pieris, Markus Schneider

AI总结:

针对组合复杂度下操作型CQA的高效近似存在性开放问题,证明有界广义超树宽无自连接合取查询下存在近似,提出SpanTL复杂度类并将相关问题归入其中。

摘要:

操作型一致性查询应答(CQA)是一种基于修复修订定义的新型CQA框架,其修复过程为:从不一致的数据库出发,应用一系列操作(如事实删除),直至得到一个满足给定约束集的一致性数据库。近期研究表明,当聚焦于主键、合取查询且假设查询固定(即数据复杂度场景下)时,存在高效近似方法可计算蕴含给定查询的修复占比,以及生成修复的操作序列占比。但当查询属于输入的一部分(即组合复杂度场景下)时,这类近似方法是否存在仍是待解问题。我们证明,当聚焦于有界广义超树宽的无自连接合取查询时,该类近似方法存在。我们还证明,一旦放弃所采用的任一语法限制(即无自连接或广义超树宽有界),高效近似方案就不太可能存在。为实现目标近似方案,我们提出了一种名为SpanTL的新型计数复杂度类,利用树自动机领域最新的可近似性结果证明SpanTL中的每个问题都存在高效近似方案,随后将关注的问题归入SpanTL类。

英文摘要:

Operational consistent query answering (CQA) is a recent framework for CQA based on revised definitions of repairs, which are built by applying a sequence of operations (e.g., fact deletions) starting from an inconsistent database until we reach a database that is consistent w.r.t. the given set of constraints. It has been recently shown that there are efficient approximations for computing the percentage of repairs, as well as of sequences of operations leading to repairs, that entail a given query when we focus on primary keys, conjunctive queries, and assuming the query is fixed (i.e., in data complexity). However, it has been left open whether such approximations exist when the query is part of the input (i.e., in combined complexity). We show that this is the case when we focus on self-join-free conjunctive queries of bounded generelized hypertreewidth. We also show that it is unlikely that efficient approximation schemes exist once we give up one of the adopted syntactic restrictions, i.e., self-join-freeness or bounding the generelized hypertreewidth. Towards the desired approximation schemes, we introduce a novel counting complexity class, called SpanTL, show that each problem in SpanTL admits an efficient approximation scheme by using a recent approximability result in the context of tree automata, and then place the problems of interest in SpanTL.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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