Résultats du processus de synthèse de l'article arXiv : 2204.00525v1

Résumé : This paper introduces Figaro, an algorithm for computing the upper-triangular matrix in the QR decomposition of the matrix defined by the natural join over a relational database. The QR decomposition lies at the core of many linear algebra techniques and their machine learning applications, including: the matrix inverse; the least squares; the singular value decomposition; eigenvalue problems; and the principal component analysis. 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 input size relative to the join size. In experiments with real-world and synthetic databases, Figaro outperforms both in runtime performance and accuracy the LAPACK libraries openblas and Intel MKL by a factor proportional to the gap between the join output and input sizes.