Givens Rotations for QR Decomposition, SVD and PCA over Database Joins

TL;DR

This paper presents Figaro, an algorithm that efficiently computes QR decomposition, SVD, and PCA directly over relational joins without materializing the join output, improving performance and accuracy.

Contribution

It introduces a novel method to push QR decomposition past database joins, enabling linear-time computation independent of join size for acyclic joins.

Findings

Figaro outperforms LAPACK Intel MKL in runtime.

It maintains numerical accuracy comparable to classical algorithms.

The method is efficient for large database joins.

Abstract

This article introduces Figaro, an algorithm for computing the upper-triangular matrix in the QR decomposition of the matrix defined by the natural join over relational data. Figaro's main novelty is that it pushes the QR decomposition past the join. This leads to several desirable properties. For acyclic joins, it takes time linear in the database size and independent of the join size. Its execution is equivalent to the application of a sequence of Givens rotations proportional to the join size. Its number of rounding errors relative to the classical QR decomposition algorithms is on par with the database size relative to the join output size. The QR decomposition lies at the core of many linear algebra computations including the singular value decomposition (SVD) and the principal component analysis (PCA). We show how Figaro can be used to compute the orthogonal matrix in the QR decomposition, the SVD and the PCA of the join output without the need to materialize the join output. A suite of experiments validate that Figaro can outperform both in runtime performance and numerical accuracy the LAPACK library Intel MKL by a factor proportional to the gap between the sizes of the join output and input.