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Opportunistic View Materialization with Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
Carefully selected materialized views can greatly improve the performance of OLAP workloads. We study using deep reinforcement learning to learn adaptive view materialization and eviction policies. Our insight is that such selection policies can be effectively trained with an asynchronous RL algorithm, that runs paired counter-factual experiments during system idle times to evaluate the incremental value of persisting certain views. Such a strategy obviates the need for accurate cardinality
arXiv:1903.01363v1
fatcat:hgpngvm6ubdpvgrtzd6ykpep7q