23 citations · 30 across the 3 of their papers we have counts for
3 papers
An Empirical Analysis of Deep Learning for Cardinality Estimation
Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke +1
We implement and evaluate deep learning for cardinality estimation by studying the accuracy, space and time trade-offs across several architectures. We find that simple deep learni…
Learning State Representations for Query Optimization with Deep Reinforcement Learning
Jennifer Ortiz, Magdalena Balazinska, Johannes Gehrke +1
Deep reinforcement learning is quickly changing the field of artificial intelligence. These models are able to capture a high level understanding of their environment, enabling the…
PerfEnforce: A Dynamic Scaling Engine for Analytics with Performance Guarantees
Jennifer Ortiz, Brendan Lee, Magdalena Balazinska +1
In this paper, we present PerfEnforce, a scaling engine designed to enable cloud providers to sell performance levels for data analytics cloud services. PerfEnforce scales a cluste…