包查询的可伸缩执行引擎

Matteo Brucato, A. Abouzeid, A. Meliou
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引用次数: 4

摘要

许多现代应用程序和现实世界的问题都涉及物品集合或包装的设计:从计划日常膳食到绘制宇宙地图。尽管对包的需求普遍存在,但传统的数据管理并不支持包的定义和计算。这是因为传统的数据库查询遵循一个功能强大但非常简单的模型:查询定义结果中的每个元组必须满足的约束。然而,一个负责包装设计的系统不能独立地考虑项目;相反,系统需要确定一组项目是否总体上满足给定的标准。在本文中,我们提出了包查询,这是一种新的查询模型,它扩展了传统的数据库查询,以处理复杂的约束和答案集的偏好。我们开发了一个成熟的包查询系统,实现在传统的数据库引擎之上。我们的工作有几个贡献。首先,我们设计PaQL,这是一种基于sql的查询语言,支持包查询的声明性规范。其次,我们提出了一种评估包查询的基本策略,该策略结合了数据库和约束优化求解器的功能。我们方法的核心是一组转换规则,将包查询转换为整数线性程序。第三,我们引入了一种离线数据分区策略,允许查询评估扩展到大数据大小。第四,我们引入了SKETCHREFINE算法,这是一种高效且可扩展的包评估算法,它提供了强大的近似保证。最后,我们对真实世界的数据进行了广泛的实验。我们的结果表明,SKETCHREFINE在获得高质量的包结果方面是有效的,并且实现的运行时性能比直接在大型数据集上使用ILP求解器快一个数量级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Scalable Execution Engine for Package Queries
Many modern applications and real-world problems involve the design of item collections, or packages: from planning your daily meals all the way to mapping the universe. Despite the pervasive need for packages, traditional data management does not offer support for their definition and computation. This is because traditional database queries follow a powerful, but very simple model: a query defines constraints that each tuple in the result must satisfy. However, a system tasked with the design of packages cannot consider items independently; rather, the system needs to determine if a set of items collectively satisfy given criteria. In this paper, we present package queries, a new query model that extends traditional database queries to handle complex constraints and preferences over answer sets. We develop a full-fledged package query system, implemented on top of a traditional database engine. Our work makes several contributions. First, we design PaQL, a SQL-based query language that supports the declarative specification of package queries. Second, we present a fundamental strategy for evaluating package queries that combines the capabilities of databases and constraint optimization solvers. The core of our approach is a set of translation rules that transform a package query to an integer linear program. Third, we introduce an offline data partitioning strategy allowing query evaluation to scale to large data sizes. Fourth, we introduce SKETCHREFINE, an efficient and scalable algorithm for package evaluation, which offers strong approximation guarantees. Finally, we present extensive experiments over real-world data. Our results demonstrate that SKETCHREFINE is effective at deriving high-quality package results, and achieves runtime performance that is an order of magnitude faster than directly using ILP solvers over large datasets.
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