Semantic Scholar Open Access 2018 468 sitasi

Learned Cardinalities: Estimating Correlated Joins with Deep Learning

Andreas Kipf Thomas Kipf Bernhard Radke Viktor Leis P. Boncz +1 lainnya

Lihat Sumber

Abstrak

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization.

Topik & Kata Kunci

Computer Science

Penulis (6)

Andreas Kipf

Thomas Kipf

Bernhard Radke

Viktor Leis

P. Boncz

A. Kemper

Format Sitasi

APA MLA BibTeX

Kipf, A., Kipf, T., Radke, B., Leis, V., Boncz, P., Kemper, A. (2018). Learned Cardinalities: Estimating Correlated Joins with Deep Learning. https://www.semanticscholar.org/paper/287050dc91b146768c9d4435e5582fc9975ba84c

Akses Cepat

PDF tidak tersedia langsung

Cek di sumber asli →

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 468×
Sumber Database: Semantic Scholar
Akses: Open Access ✓