Our paper about learned Database partitioning was accepted to aiDM 2019

In this paper we introduce a partitioning advisor for analytical workloads based on Deep Reinforcement Learning.

In contrast to existing approaches for automated partitioning design, an RL agent learns its decisions based on experience by trying out different partitioning schemas and monitoring the rewards for different workloads.

In our experimental evaluation, we show that our learned partitioning advisor is thus not only able to find partitionings that outperform existing approaches for automated data partitioning but is also able to find non-obvious partitionings.