Abstract: A data store system may include at least one storage device to store a plurality of data and at least one processor with access to the storage device. The at least one processor may receive a plurality of features associated with an environment. The at least one processor may further generate a state representation of the environment based on the plurality of features. The at least one processor may further generate a plurality of predicted future states of the environment based on the state representation. The at least one processor may further generate at least one action to be performed by the environment based on the plurality of predicted future states. The at least one processor may provide the at least one action to the environment to be performed. A method and computer-readable medium are also disclosed.

Abstract: A method is provided for predicting workload group metrics of a workload management system of a database system. The method comprises predicting a future workload group metric for a plurality of workload groups based upon historical user-load patterns. Each workload group has a priority that is different from priority of other workload groups.

Abstract: A query is preprocessed for features identified by a Data Manipulation Language (DML) in the text of the query. A machine-learning algorithm uses the features as input and provides as output a predicted query parsing execution time needed by a query parser to parse the query. The predicted query parsing time is provided as input to a query optimizer. The query optimizer uses the predicted query parsing time as a factor in optimizing a query execution plan for the query. Subsequently, the query execution plan is executed against a database as the query.