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Optimizing subset queries: a step towards SQL-based inductive databases for itemsets

机译:优化子集查询:迈向基于SQL的项目集归纳数据库的一步

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摘要

Storing sets and querying them (e.g., subset queries that provide all supersets of a given set) is known to be difficult within relational databases. We consider that being able to query efficiently both transactional data and materialized collections of sets by means of standard query language is an important step towards practical inductive databases. Indeed, data mining query languages like MINE RULE extract collections of association rules whose components are sets into relational tables. Post-processing phases often use extensively subset queries and cannot be efficiently processed by SQL servers. In this paper, we propose a new way to handle sets from relational databases. It is based on a data structure that partially encodes the inclusion relationship between sets. It is an extension of the hash group bitmap key proposed by Morzy et al. [8]. Our experiments show an interesting improvement for these useful subset queries.
机译:已知在关系数据库中难以存储和查询集合(例如,提供给定集合的所有超集的子集查询)。我们认为,能够通过标准查询语言有效地查询交易数据和物化集合集合是迈向实用归纳数据库的重要一步。确实,像MINE RULE这样的数据挖掘查询语言会提取关联规则的集合,这些集合的组成部分设置在关系表中。后处理阶段通常使用大量的子集查询,并且SQL服务器无法有效地对其进行处理。在本文中,我们提出了一种处理关系数据库中的集合的新方法。它基于部分编码集之间包含关系的数据结构。它是Morzy等人提出的哈希组位图密钥的扩展。 [8]。我们的实验显示了这些有用的子集查询的有趣改进。

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