关系查询合成 ⋈ 决策树学习

Aaditya Naik, Aalok Thakkar, Adam Stein, R. Alur, Mayur Naik
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引用次数: 0

摘要

我们研究了从输入-输出示例中合成关系查询核心片段(称为选择-项目-连接(SPJ)查询)的问题。基于搜索的合成技术适合通过浏览关系表网络来合成投影和连接,但在合成比较谓词时需要额外的监督。另一方面,决策树学习技术适合在输入数据库可归纳为单一标记关系表的情况下合成比较谓词。在本文中,我们对关系查询合成和决策树学习领域的方法进行了调整和交错,并提出了一个端到端的框架,用于合成带有分类和数字比较谓词的关系查询。我们的技术保证了合成过程的完整性,并强烈鼓励合成程序的最小化。我们介绍了这项技术的实现--Libra,并在 159 个多表数据库的 1475 个查询实例的基准套件上对其进行了评估。Libra 解决了其中的 1,361 个实例,平均每个实例耗时 59 秒。在运行时间和合成程序的质量方面,它都优于最先进的程序合成工具 Scythe 和 PatSQL。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Relational Query Synthesis ⋈ Decision Tree Learning
We study the problem of synthesizing a core fragment of relational queries called select-project-join (SPJ) queries from input-output examples. Search-based synthesis techniques are suited to synthesizing projections and joins by navigating the network of relational tables but require additional supervision for synthesizing comparison predicates. On the other hand, decision tree learning techniques are suited to synthesizing comparison predicates when the input database can be summarized as a single labelled relational table. In this paper, we adapt and interleave methods from the domains of relational query synthesis and decision tree learning, and present an end-to-end framework for synthesizing relational queries with categorical and numerical comparison predicates. Our technique guarantees the completeness of the synthesis procedure and strongly encourages minimality of the synthesized program. We present Libra, an implementation of this technique and evaluate it on a benchmark suite of 1,475 instances of queries over 159 databases with multiple tables. Libra solves 1,361 of these instances in an average of 59 seconds per instance. It outperforms state-of-the-art program synthesis tools Scythe and PatSQL in terms of both the running time and the quality of the synthesized programs.
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