面向支持偏好的关系数据库

Anastasios Arvanitis, G. Koutrika
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引用次数: 23

摘要

在实现感知偏好的查询处理时,一个直接的选择是在数据库引擎之上构建一个插件。然而,将DBMS视为黑盒会影响带有首选项的查询的表现力和性能。在本文中,我们认为偏好感知查询处理需要更接近DBMS。我们提出了一个具有偏好感知的关系数据模型,该模型扩展了具有偏好的数据库元组,并提供了一个扩展代数,该代数捕获了处理具有偏好的查询的本质。我们的偏好模型本身的一个关键新颖之处在于,它在三个维度上定义了偏好,显示了受影响的元组、它们的偏好得分和偏好的可信度。我们的查询处理策略将首选项评估推入查询计划,并利用其代数属性进行更细粒度的查询优化。我们通过实验对所提出的策略进行了评估。最后,我们将我们的框架与纯插件实现进行了比较,并展示了其可行性和优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Preference-aware Relational Databases
In implementing preference-aware query processing, a straightforward option is to build a plug-in on top of the database engine. However, treating the DBMS as a black box affects both the expressivity and performance of queries with preferences. In this paper, we argue that preference-aware query processing needs to be pushed closer to the DBMS. We present a preference-aware relational data model that extends database tuples with preferences and an extended algebra that captures the essence of processing queries with preferences. A key novelty of our preference model itself is that it defines a preference in three dimensions showing the tuples affected, their preference scores and the credibility of the preference. Our query processing strategies push preference evaluation inside the query plan and leverage its algebraic properties for finer-grained query optimization. We experimentally evaluate the proposed strategies. Finally, we compare our framework to a pure plug-in implementation and we show its feasibility and advantages.
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