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

机译:优化子集查询:迈出基于SQL的归纳数据库的步骤

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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:在关系数据库中,已知存储集并查询它们(例如,提供给定集中的所有超级集的子集查询)。 我们认为,通过标准查询语言可以有效地查询事务性数据和集合集的集合是迈向实际归纳数据库的重要步骤。 实际上,数据挖掘Querges语言如Lime Rure提取的关联规则集合,其组件被设置为关系表。 后处理阶段通常使用广泛的子集查询,并且无法通过SQL服务器有效处理。 在本文中,我们提出了一种从关系数据库处理集合的新方法。 它基于数据结构,该数据结构部分编码集合之间的包含关系。 它是Morzy等人提出的哈希组位图密钥的扩展。 [8]。 我们的实验表明了这些有用的子集查询的有趣改进。

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