Size Bounds for Factorised Representations of Query Results

IF 2.2 2区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on Database Systems Pub Date : 2015-03-25 DOI:10.1145/2656335
Dan Olteanu, Jakub Závodný
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引用次数: 118

Abstract

We study two succinct representation systems for relational data based on relational algebra expressions with unions, Cartesian products, and singleton relations: f-representations, which employ algebraic factorisation using distributivity of product over union, and d-representations, which are f-representations where further succinctness is brought by explicit sharing of repeated subexpressions. In particular we study such representations for results of conjunctive queries. We derive tight asymptotic bounds for representation sizes and present algorithms to compute representations within these bounds. We compare the succinctness of f-representations and d-representations for results of equi-join queries, and relate them to fractional edge covers and fractional hypertree decompositions of the query hypergraph. Recent work showed that f-representations can significantly boost the performance of query evaluation in centralised and distributed settings and of machine learning tasks.
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查询结果的分解表示的大小界限
我们研究了基于关系代数表达式的关系数据的两个简洁的表示系统,这些关系代数表达式具有联合、笛卡尔积和单例关系:f表示,它使用乘积在并上的分布来进行代数分解;d表示,它是f表示,其中通过显式地共享重复子表达式来进一步简化。我们特别研究了连接查询结果的这种表示。我们推导了表示大小的紧渐近边界,并给出了在这些边界内计算表示的算法。我们比较了等价连接查询结果的f-表示和d-表示的简洁性,并将它们与查询超图的分数阶边覆盖和分数阶超树分解联系起来。最近的研究表明,f-表示可以显著提高集中和分布式设置以及机器学习任务的查询评估性能。
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来源期刊
ACM Transactions on Database Systems
ACM Transactions on Database Systems 工程技术-计算机:软件工程
CiteScore
5.60
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Heavily used in both academic and corporate R&D settings, ACM Transactions on Database Systems (TODS) is a key publication for computer scientists working in data abstraction, data modeling, and designing data management systems. Topics include storage and retrieval, transaction management, distributed and federated databases, semantics of data, intelligent databases, and operations and algorithms relating to these areas. In this rapidly changing field, TODS provides insights into the thoughts of the best minds in database R&D.
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