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arXiv 2303.08583cs.DB
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F-IVM:更新场景下的关系数据库分析

F-IVM: Analytics over Relational Databases under Updates

Ahmet Kara, Milos Nikolic, Dan Olteanu, Haozhe Zhang

AI总结:

本研究提出F-IVM统一框架,融合高阶增量视图维护、因子化计算与环抽象,可高效处理更新场景下关系数据库的多类分析任务,性能较传统方法提升数个数量级且内存占用更低。

摘要:

本文介绍了F-IVM,这是一种在不断变化的关系数据上维护分析任务的统一方法。我们在四个领域中展示了其通用性:处理带有分组聚合和连接的查询;利用输入特征的协方差矩阵学习线性回归模型;利用输入特征的成对互信息构建Chow-Liu树;以及矩阵链乘法。\nF-IVM包含三个核心要素:高阶增量视图维护、因子化计算以及环抽象。F-IVM将一项任务的维护工作简化为一系列简单视图的层级维护。这类视图是将键(即输入值构成的元组)映射到有效载荷(即环中的元素)的函数。F-IVM还支持针对键、有效载荷以及更新操作的高效因子化计算。最后,F-IVM可统一处理看似截然不同的任务。在键空间中,所有任务都需要执行连接操作和变量边缘化;在有效载荷空间中,各任务的区别在于加法和乘法环运算的定义不同。\n我们在DBToaster之上实现了F-IVM,实验表明,其性能比经典的一阶以及全递归高阶增量视图维护高出数个数量级,同时内存占用更低。

英文摘要:

This article describes F-IVM, a unified approach for maintaining analytics over changing relational data. We exemplify its versatility in four disciplines: processing queries with group-by aggregates and joins; learning linear regression models using the covariance matrix of the input features; building Chow-Liu trees using pairwise mutual information of the input features; and matrix chain multiplication. F-IVM has three main ingredients: higher-order incremental view maintenance; factorized computation; and ring abstraction. F-IVM reduces the maintenance of a task to that of a hierarchy of simple views. Such views are functions mapping keys, which are tuples of input values, to payloads, which are elements from a ring. F-IVM also supports efficient factorized computation over keys, payloads, and updates. Finally, F-IVM treats uniformly seemingly disparate tasks. In the key space, all tasks require joins and variable marginalization. In the payload space, tasks differ in the definition of the sum and product ring operations. We implemented F-IVM on top of DBToaster and show that it can outperform classical first-order and fully recursive higher-order incremental view maintenance by orders of magnitude while using less memory.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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