157 citations · 191 across the 6 of their papers we have counts for
5 papers · 1 filter
Estimating Cardinalities with Deep Sketches
Andreas Kipf, Dimitri Vorona, Jonas Müller +6
We introduce Deep Sketches, which are compact models of databases that allow us to estimate the result sizes of SQL queries. Deep Sketches are powered by a new deep learning approa…
Learned Cardinalities: Estimating Correlated Joins with Deep Learning
Andreas Kipf, Thomas Kipf, Bernhard Radke +3
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 sem…
Adaptive Geospatial Joins for Modern Hardware
Andreas Kipf, Harald Lang, Varun Pandey +4
Geospatial joins are a core building block of connected mobility applications. An especially challenging problem are joins between streaming points and static polygons. Since point…
G-CORE: A Core for Future Graph Query Languages
Renzo Angles, Marcelo Arenas, Pablo Barceló +9
We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider…
From Cooperative Scans to Predictive Buffer Management
Michał Świtakowski, Peter Boncz, Marcin Żukowski
In analytical applications, database systems often need to sustain workloads with multiple concurrent scans hitting the same table. The Cooperative Scans (CScans) framework, which…