Abstract: A computing device, method, and system are provided of improving data quality to conserve computational resources. The computing device receives a raw dataset. One or more data quality metric goals corresponding to the received raw dataset are received. A schema of the dataset is determined. An initial set of validation nodes is identified based on the schema of the dataset. The initial set of validation nodes are executed. A next set of validation nodes are iteratively expanded and executed based on the schema of the dataset until a termination criterion is reached. A corrected dataset of the raw dataset is provided based on the iterative execution of the initial and next set of validation nodes.

Abstract: Aspects of the invention include a computer-implemented method including receiving, using a processor, a plurality of input process variables and a plurality of output process variables associated with a respective plurality of processes. The processor is used to create an optimal decision tree based on the plurality of input variables, plurality of output variables, and plurality of processes. For each of the plurality of processes, intermediate quality modes and corresponding controls are identified. The optimal decision tree is trained based on the identified intermediate quality modes and corresponding controls. Recommended control variable values are provided for each of the plurality of processes.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.