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Found 5 papers, 1 papers with code
Multi-agent Databases via Independent Learning
Thus, the paper attempts to address the question "Is it possible to design a database consisting of various learned components that cooperatively work to improve end-to-end query latency?".
Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning
In this extended abstract, we propose a new technique for query scheduling with the explicit goal of reducing disk reads and thus implicitly increasing query performance.
Flexible Operator Embeddings via Deep Learning
Integrating machine learning into the internals of database management systems requires significant feature engineering, a human effort-intensive process to determine the best way to represent the pieces of information that are relevant to a task.
Deep Reinforcement Learning for Join Order Enumeration
However, modern query optimizers typically employ static join enumeration algorithms that do not receive any feedback about the quality of the resulting plan.
WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases
Workload management for cloud databases must deal with the tasks of resource provisioning, query placement and query scheduling in a manner that meets the application's performance goals while minimizing the cost of using cloud resources.
Databases
