论文标题
关系矩阵代数及其在列存储中的实现
A Relational Matrix Algebra and its Implementation in a Column Store
论文作者
论文摘要
分析查询通常需要使用应用于相同数据的关系和线性代数操作的混合物。这对必须弥合关系和矩阵之间差距的分析系统提出了挑战。以前的工作主要努力在实施级别解决该问题。本文在逻辑层面提出了一种原则性解决方案。我们介绍了关系矩阵代数（RMA），该代数将线性代数操作无缝整合到关系模型中，并消除了矩阵与关系之间的二分法。 RMA已关闭：我们所有的关系矩阵操作都是在关系上进行的，并导致关系；无需其他数据结构。我们在MONETDB中的实施表明了我们的方法的可行性，并且经验评估表明，数据库分析对于混合工作量的表现良好。
Analytical queries often require a mixture of relational and linear algebra operations applied to the same data. This poses a challenge to analytic systems that must bridge the gap between relations and matrices. Previous work has mainly strived to fix the problem at the implementation level. This paper proposes a principled solution at the logical level. We introduce the relational matrix algebra (RMA), which seamlessly integrates linear algebra operations into the relational model and eliminates the dichotomy between matrices and relations. RMA is closed: All our relational matrix operations are performed on relations and result in relations; no additional data structure is required. Our implementation in MonetDB shows the feasibility of our approach, and empirical evaluations suggest that in-database analytics performs well for mixed workloads.
