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DB-BERT extracts tuning hints for database systems from text documents such as the manual. It uses extracted hints as a starting point for automated performance tuning.

To analyze text, DB-BERT uses pre-trained language models such as BERT. During tuning, DB-BERT iteratively selects parameter settings and measures performance on a user-defined benchmark. It selects settings to try via reinforcement learning, integrating information extracted from text as well as performance measurements in past iterations. By exploiting text as additional input, DB-BERT tends to find promising configurations faster than baselines.